Frailty—a multidimensional syndrome characterized by diminished strength, endurance, and physiological function—is notoriously challenging to predict accurately. Its presence significantly elevates the risk of adverse health events such as falls, hospitalization, and mortality, particularly among elderly individuals with CKD. Traditional predictive models often rely on cross-sectional data and superficial correlations, which while informative, fail to fully capture the nuanced causal relationships that drive frailty progression. The study under discussion addresses this critical limitation by harnessing causal feature learning, a method that goes beyond association to identify features with direct influence on patient outcomes.

What sets this research apart is its commitment to individualized prediction. Recognizing that frailty manifests differently across patients due to genetic, environmental, and comorbid condition variabilities, the AI framework is designed to personalize risk profiles. Embedded causal feature extraction allows the model to discern which factors hold genuine predictive power for a given individual, such as specific biomarkers, clinical history elements, or lifestyle parameters. This granularity is essential for developing interventions that are not only effective but also patient-centric and ethically sound.

The methodology integrates advanced machine learning architectures that perform knowledge distillation—a process where a complex, highly accurate model (the “teacher”) transfers its learned knowledge to a simpler, more interpretable model (the “student”). This approach ensures that the final predictive framework is both powerful and usable in real-world clinical environments. Clinicians can thus benefit from transparent decision-support tools without sacrificing predictive precision, bridging the notorious “black box” gap that often hampers AI’s clinical adoption.

Furthermore, the causal learning backbone enhances the model’s robustness against confounding variables and biases commonly encountered in medical datasets. By identifying true causal relationships rather than merely correlational patterns, the AI-driven framework promises resilience when applied to diverse patient populations and external validation cohorts. This addresses a critical bottleneck in medical AI—generalizability—which is paramount for any tool aiming for widespread clinical implementation.

The frailty prediction initiative detailed in this protocol also features a dynamic intervention component. Leveraging the rich causal insights, the system not only forecasts frailty risk but actively informs tailored therapeutic strategies. These interventions might include optimized pharmacological regimens, personalized nutrition plans, or specific physical rehabilitation protocols that align directly with each patient’s unique frailty determinants. This adaptive feedback loop exemplifies the shift toward precision medicine, wherein AI systems do not merely assess risk but empower proactive, individualized care planning.

Mounting evidence underscores the heavy toll of chronic kidney disease on elderly populations, where frailty accelerates morbidity and complicates management. By embedding AI at the intersection of nephrology and geriatric care, this research ventures into uncharted territory. It aims to capture the multifactorial etiology of frailty with unprecedented clarity, enabling healthcare providers to anticipate and mitigate decline before clinical deterioration occurs. This proactive stance could substantially reduce healthcare costs while improving quality of life for some of the most vulnerable patients.

Clinical datasets feeding the AI framework are meticulously curated, integrating longitudinal data from electronic health records, laboratory results, imaging, and patient-reported outcomes. The large-scale, multi-center nature of these datasets enriches the AI’s learning capacity and supports the extraction of reliable causal signals amidst noise and variability. This extensive data fusion epitomizes modern health informatics, where synergy between diverse data types fuels next-generation predictive analytics.

Importantly, the research team has planned rigorous validation phases, encompassing retrospective analyses and prospective clinical trials. Such stringent testing is vital to ensure the system’s efficacy and safety before deployment. Ethical considerations also accompany this innovation, with explicit attention to patient consent, data privacy, and algorithmic transparency. These safeguards promote trust among both patients and practitioners, a key factor for successful AI integration in sensitive areas like frailty assessment.

The potential impact of this AI-powered prediction and intervention framework extends beyond nephrology and geriatrics. By demonstrating how causal inference and knowledge distillation can coalesce in personalized medicine, the study sets a precedent for analogous applications in other chronic conditions where frailty and functional decline are prevalent, such as chronic obstructive pulmonary disease, heart failure, and neurodegenerative diseases.

As AI continues to reshape healthcare landscapes, this protocol highlights the critical symbiosis between cutting-edge data science and clinical insight. The collaborative effort between computer scientists, nephrologists, geriatricians, and bioinformaticians has produced a model that respects the complexity of human biology while offering scalable solutions to pressing clinical challenges. Such multidisciplinary synergy is a hallmark of future-proof innovations destined to thrive in the 21st-century healthcare ecosystem.

In summary, the advent of an AI-driven individualized frailty prediction and intervention framework represents a transformative advancement for elderly patients grappling with chronic kidney disease. Through causal feature learning and knowledge-distillation, the framework achieves a nuanced understanding of frailty drivers, empowering personalized preventative strategies and precision care. Beyond its immediate clinical promise, this research exemplifies how sophisticated AI methodologies can be responsibly harnessed to tackle multifaceted medical problems, fostering healthier aging populations worldwide.

Subject of Research: Development of an AI-driven individualized frailty prediction and intervention framework for elderly patients with chronic kidney disease using causal feature learning and knowledge-distillation-based modeling.

Article Title: Protocol for development of an AI-driven individualized frailty prediction and intervention framework for elderly patients with chronic kidney disease: causal feature learning and knowledge-distillation-based modeling study.

Article References:
Chang, J., Hu, J., Cao, Y. et al. Protocol for development of an AI-driven individualized frailty prediction and intervention framework for elderly patients with chronic kidney disease: causal feature learning and knowledge-distillation-based modeling study. BMC Geriatr (2026). https://doi.org/10.1186/s12877-026-07143-0

Image Credits: AI Generated

OMARI’s mission is unequivocal: it aims to harness the transformative potential of artificial intelligence in medical applications. By serving as a centralized resource hub, the institute is set to expedite groundbreaking research discoveries, enhance educational opportunities for students, and leverage data-driven tools to achieve greater health equity. The institute is poised to revolutionize the approach to medical research by incorporating AI technologies that have previously had limited integration into clinical settings.

The institute’s foundation rests on an ambitious vision to showcase the implementation power of AI through strategic partnerships with the University of Ottawa’s esteemed affiliated hospitals and research institutions. By bridging the gap between theoretical research and practical applications, OMARI is designed to cultivate collaborative teams where innovation thrives, enabling medical students to emerge as pioneers in medical AI.

Dr. El Emam emphasizes the institute’s role in encouraging clinicians, researchers, and students to transition their laboratory innovations into real-world applications. He believes that innovation and commercialization should not be separate endeavors; rather, they should complement and enhance each other. This perspective is particularly relevant in the medical field, where the need for immediate impact is often paramount.

Through OMARI, researchers will have the unique opportunity to develop and spin-off their companies directly from their labs. This initiative is expected to accelerate the commercialization of cutting-edge medical AI applications, bringing innovative solutions to market swiftly. Additionally, OMARI will identify and promote non-traditional funding sources that are currently under-utilized, including philanthropic organizations and specific foundations dedicated to medical AI advancements.

The institute also intends to create a collaborative ecosystem, dubbed “communities of practice,” where investigators and students engaged in similar research domains can share insights and support one another. This collaborative framework will not only stimulate innovative thinking but also enhance the overall quality of research outputs, fostering a culture of continuous improvement and competitiveness within the medical AI arena.

OMARI’s initial focus is to advance medical research through the ethical deployment of AI tools while also integrating educational initiatives to prepare future generations of medical professionals. As part of this, the institute aims to equip students with not only foundational knowledge but also the necessary skills to utilize AI in their problem-solving approaches effectively. This aligns with the current demands of the industry, where speed and efficiency are critical in delivering timely healthcare solutions.

In addition to teaching fundamental concepts, OMARI plans to delve into advanced educational techniques by integrating AI into the learning process itself. Dr. El Emam envisions AI as a valuable ally in enhancing educational outcomes, allowing students to code more efficiently and generate analytical results with greater speed. This approach will prepare students not just as consumers of technology but as innovators capable of shaping the future of healthcare.

Moreover, OMARI’s efforts are timely and critical, especially in light of the growing recognition of AI as a transformative force in healthcare. With increasing investments and public attention directed toward AI in medicine, the institute stands to elevate Ottawa as a hub of excellence in medical research and technology. The global significance of such initiatives cannot be overstated, as they pave the way for improved health outcomes across diverse populations through the strategic application of AI.

OMARI is committed to ethical research practices that prioritize patient safety and data privacy. By establishing guidelines for ethical AI usage in medical research, the institute endeavors to be at the cutting edge of ensuring that technological advancements do not compromise the fundamental values of healthcare. This ethical framework is essential as AI technologies become more prevalent in clinical decision-making and patient care, necessitating a rigorous approach to governance and accountability.

In conclusion, the launch of the Ottawa Medical Artificial Intelligence Research Institute represents a monumental step in the intersection of healthcare and technology, embodying the potential of AI to revolutionize medical practices and education. Through its comprehensive mission, OMARI not only aims to enhance the university’s competitiveness but also strives to impact community health outcomes positively. As the institute embarks on this transformative journey, it stands as a beacon of innovation and collaboration, inspiring a new generation of healthcare professionals to harness the power of artificial intelligence for the greater good.

Subject of Research:
Article Title: University of Ottawa Launches Medical Hub for AI-Driven Health Breakthroughs
News Publication Date: [Insert Date]
Web References: [Insert Relevant URLs]
References: [Insert any references used]
Image Credits: Credit: University of Ottawa

Keywords

The rapid advancement in AI technologies has generated a palpable excitement within the medical community, particularly regarding their potential to transform patient care. As AI systems become more sophisticated, they are increasingly being viewed not merely as tools for data analysis but as partners in clinical decision-making. This study seeks to address a pivotal question—can these sophisticated AI models effectively assist in assessing knowledge and providing reliable solutions in the dental field, particularly for patients who present additional medical challenges?

In the context of this research, the AI models used—GPT-5, DeepSeek, and Claude—each bring unique methodologies to the table. GPT-5, for instance, is renowned for its extensive training datasets and its ability to generate coherent and contextually appropriate responses to a wide array of queries. DeepSeek, while less publicized, utilizes deep learning techniques aimed at enhancing understanding of complex medical scenarios. Lastly, Claude has garnered attention for its innovative approach to parsing information, particularly pertinent to clinical settings. The combination of these diverse AI models provides a comprehensive overview of how machine learning can revolutionize the approach to patient assessments in dentistry.

The study’s inclusion of medically compromised patients is particularly noteworthy. This demographic often presents unique challenges due to their intricate health situations, which necessitate a nuanced approach to dental treatment. Conditions such as diabetes, cardiovascular disease, and immunocompromised states can significantly complicate dental procedures. Thus, evaluating the capacity of AI models to navigate these complexities underscores the practical implications of this research. Would these models provide reliable answers in a high-stakes environment?

Diving into the methodology, the authors structured the research around a set of well-crafted MCQs that reflect real-world scenarios dental practitioners may face when treating medically compromised patients. The questions were designed not only to assess knowledge of standard dental practices but also to evaluate the understanding of how various systemic conditions can influence dental treatment outcomes. By employing these realistic and challenging scenarios, the investigators aimed to push the boundaries of what AI can achieve in this specialized domain.

The results of the study revealed some intriguing findings. Each of the AI models demonstrated varying degrees of success in answering the MCQs accurately. GPT-5 remarkably excelled in providing comprehensive answers that incorporated the latest research and guidelines on dental care for medically compromised individuals. This ability to synthesize information from diverse sources and produce well-rounded responses marks a significant step toward enhancing AI’s role in clinical diagnostics.

Conversely, while DeepSeek exhibited proficiency in regional problem-solving related to dental issues, it struggled with more intricate patient management questions that required a multifaceted understanding of patient health history. This shortfall highlights an important consideration in the deployment of AI in clinical settings: while advanced models can offer valuable insights, they may not fully replace the nuanced decision-making that experienced clinicians bring to practice. The challenge remains to fine-tune these models to bridge these gaps and produce robust answers.

Claude’s performance, while noteworthy, presented a mixed bag of results. It excelled in providing quick and intuitive answers but occasionally faltered in the depth of its responses. This inconsistency may point to the need for further refinements and training to enhance the sophistication of Claude’s knowledge base. It reinforces the takeaway that while AI can indeed assist in the medical field, layers of complexity remain that require ongoing exploration.

The implications of this study extend beyond merely assessing the performance of AI in dental MCQs; they frame a broader narrative of how technology can enhance patient safety and care. As AI systems are continually refined and improved, their integration into daily practice could lead to more personalized treatment plans, particularly for patients with specific health conditions that warrant heightened vigilance.

Moreover, the evolving dialogue surrounding the ethical implications of using AI in healthcare cannot be overstated. As these technologies develop, healthcare professionals face the pressing need to effectively integrate AI tools into their workflows while maintaining a focus on patient-centric care. Training and preparation for healthcare providers must be prioritized, as they will ultimately be the ones navigating the dual landscape of AI capabilities and patient needs.

Ultimately, the findings of Altos, Awad, and Bashah’s research serve as both an accomplishment and a call to action. They invite ongoing collaboration among AI developers, healthcare professionals, and researchers to continue pushing the boundaries of what can be achieved in clinical environments. The prospect of AI-assisted decision-making in dentistry, particularly for medically compromised patients, offers a glimpse into the future of integrated health technologies that aim to enhance treatment efficiency, effectiveness, and patient outcomes.

In conclusion, the study of AI systems like GPT-5, DeepSeek, and Claude offers a vital perspective on the intersection of technology and healthcare within the dental realm. The potential of these tools to revolutionize how clinicians approach treatment for complex patients is evident. Still, significant work lies ahead in refining these technologies to ensure they meet the high standards required in real-world clinical practice. As research continues to unfold, it will be fascinating to observe how AI influences the future of dentistry and patient care.

Subject of Research: Performance of AI models in dental MCQs for medically compromised patients.

Article Title: Performance of GPT-5, DeepSeek, and Claude in dental MCQs for medically compromised patients.

Article References:

Altos, O., Awad, A., Bashah, A. et al. Performance of GPT-5, DeepSeek, and Claude in dental MCQs for medically compromised patients.
J Transl Med (2026). https://doi.org/10.1186/s12967-026-07763-5

Image Credits: AI Generated

DOI: 10.1186/s12967-026-07763-5

Keywords: AI in healthcare, dental care, medically compromised patients, GPT-5, DeepSeek, Claude, clinical decision-making, patient outcomes.

Echocardiography typically enables healthcare professionals to visualize the heart’s structure and function through ultrasound waves. The integration of deep learning has amplified the capabilities of echocardiography, improving both image quality and the accuracy of diagnostics. Deep learning algorithms, powered by vast datasets and sophisticated neural networks, can analyze echocardiographic images with heightened speed and precision. This offers hope for earlier detection of cardiovascular diseases, potentially resulting in better patient outcomes.

One of the most significant advantages of employing deep learning in echocardiography is the ability to extract relevant clinical information from complex datasets. Traditional image analysis often necessitates extensive manual input from highly trained professionals, which can be time-consuming and error-prone. In contrast, deep learning algorithms can automate these processes, allowing for quicker analyses with consistent results. For instance, the identification of cardiac abnormalities can be streamlined through advanced algorithms that highlight regions of interest within images, thereby guiding clinicians in their evaluations more effectively.

The clinical impacts of deep learning in echocardiography extend beyond just efficiency. They have the potential to influence treatment decisions significantly. By enhancing diagnostic accuracy, these advanced algorithms allow for more tailored treatment plans for patients experiencing various cardiac conditions. For instance, distinguishing between different types of cardiomyopathies becomes more feasible with the assistance of intelligent systems, ultimately leading to improved therapeutic strategies and patient management.

Furthermore, the challenges encountered in integrating deep learning into clinical practice must not be overlooked. Most prominently, the issue of data privacy and security looms large. The utilization of patient data to train deep learning models raises ethical concerns surrounding confidentiality and consent. Moreover, the requirement for extensive annotated datasets means that collaborations between medical institutions become essential. However, such collaborations can be hindered by competitive dynamics, differing regulatory frameworks, and logistical issues.

Another challenge lies in the interpretability of deep learning models. While these algorithms can provide accurate assessments, they often operate as black boxes, making it difficult for clinicians to understand the reasoning behind certain predictions or suggestions. As heart health is paramount, ensuring that clinicians can effectively interpret and trust these technologies is critical. Advancements in explainable AI are necessary to bridge this gap, fostering confidence among healthcare professionals in the integration of deep learning.

Moreover, regulatory hurdles need to be addressed. The healthcare industry is notorious for its stringent regulations, which can pose challenges for deploying novel technologies rapidly. As deep learning innovations continue to emerge, regulatory bodies must implement frameworks that streamline evaluation processes while ensuring safety and efficacy. Collaboration among stakeholders—including engineers, clinicians, and regulatory agencies—will be crucial to navigating these complex challenges.

Despite these hurdles, the opportunities presented by deep learning innovations in echocardiography are vast. Enhanced training methodologies can lead to more robust algorithms that not only analyze images but also predict patient outcomes. For example, integrating real-time data from other medical devices, like heart rate monitors, with echocardiographic analysis could lead to comprehensive dashboards that provide clinicians with predictive insights. This innovation may empower healthcare providers to intervene preemptively, ultimately reducing morbidity and mortality associated with heart disease.

Additionally, as technology evolves, telemedicine’s potential to complement deep learning-driven echocardiography cannot be ignored. Remote consultations enabled by streaming echocardiography images along with AI-driven analyses could transform how cardiology is practiced. This is especially relevant for patients in rural or underserved areas lacking immediate access to specialist care. By marrying deep learning with telemedicine, healthcare equity can significantly improve, allowing for comprehensive cardiac assessments regardless of geographic location.

However, as we embrace the future, training and education remain paramount. Current and future medical professionals must be equipped to navigate the evolving landscape shaped by AI and big data. Medical curricula should evolve to incorporate education on machine learning principles, enabling students and practitioners to understand not only how to use these tools but also how to critically evaluate their outputs. Empowering clinicians with knowledge will facilitate a culture of collaboration between human expertise and machine intelligence.

The importance of multidisciplinary collaboration cannot be understated in this transformation. Engineers, data scientists, and clinicians must work hand-in-hand to design, assess, and refine deep learning algorithms. This collaborative approach is essential for tailoring solutions that directly address clinical needs while maintaining high performance and reliability standards. The intersection of expertise will foster holistic approaches, allowing for innovations that benefit patients directly.

In conclusion, the intersection of deep learning and echocardiography embodies a paradigm shift in cardiovascular diagnostics. The deep learning-driven innovations promise heightened diagnostic accuracy, improved clinical decision-making, and the potential for preventive care. However, an emphasis on ethical practices, regulatory collaboration, and interdisciplinary engagement will be necessary to realize these benefits fully. As the healthcare landscape continues to evolve, embracing these changes will be essential for advancing cardiac care and ultimately saving lives.

Subject of Research: Integration of deep learning techniques in echocardiography.

Article Title: Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.

Article References:
Monkam, P., Wang, X., Liu, S. et al. Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.
Ann Biomed Eng (2025). https://doi.org/10.1007/s10439-025-03944-3

Image Credits: AI Generated

DOI: https://doi.org/10.1007/s10439-025-03944-3

Keywords: Echocardiography, deep learning, cardiovascular diagnostics, artificial intelligence, healthcare innovation.

Emerging technologies in artificial intelligence (AI), particularly machine learning (ML), are transforming the landscape of medical diagnostics, offering pathways to automate and enhance disease detection accuracy. Unlike traditional diagnostic methods reliant on overt clinical manifestations or limited biomarkers, ML algorithms excel at discerning intricate, nonlinear patterns within high-dimensional biomedical datasets. These subtle signals often elude human analysis but are critical for swift and precise diagnosis. Researchers at Florida Atlantic University’s College of Engineering and Computer Science have ventured beyond conventional ML approaches by exploring the integration of quantum computing into diagnostic frameworks for CKD. Their pioneering work seeks to evaluate how quantum-enhanced machine learning may revolutionize disease prediction accuracy and computational efficiency.

At the core of this research initiative lies a comparative analysis of two diagnostic systems: a classical Support Vector Machine (CSVM) and its quantum counterpart, the Quantum Support Vector Machine (QSVM). Both methods were applied uniformly to meticulously curated datasets representative of CKD patient profiles. Preparation of these datasets involved rigorous preprocessing steps designed to eliminate noise and standardize inputs, thereby enhancing reliability. In addition, sophisticated dimensionality reduction techniques—Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)—were employed to optimize feature spaces. These preprocessing algorithms play a crucial role in mitigating data redundancy, enhancing signal-to-noise ratio, and ultimately improving downstream classification performance and computational expediency.

The study’s findings, recently published in the journal Informatics and Health, unveiled insightful contrasts between the classical and quantum methodologies. When PCA was utilized for data optimization, the classical SVM attained a striking diagnostic accuracy of 98.75%, whereas the QSVM achieved a lower yet competitive accuracy of 87.5%. Using SVD, the gap widened further: CSVM achieved 96.25%, far outperforming the QSVM’s accuracy of 60%. Moreover, computational speed analyses favored the classical system markedly—CSVM was up to forty-two times faster in certain experimental contexts. These results underscore present-day hardware limitations inherent in quantum computing implementations, which currently hinder the full realization of quantum algorithmic potential in clinical diagnostics.

Despite the quantum model’s underperformance relative to its classical peer, researchers emphasize that this discrepancy is symptomatic of current quantum hardware constraints rather than a fundamental deficiency of quantum algorithms themselves. The QSVM’s 87.5% accuracy using PCA notably surpasses several classical SVM performances documented in prior studies, illustrating that even within current classical hardware simulations, quantum approaches exhibit promising diagnostic capabilities. This discovery lays the groundwork for hybrid quantum-classical computational architectures where the complementary strengths of each paradigm are leveraged in tandem. Such hybrid systems may optimize accuracy and robustness while pragmatically navigating the technological bottlenecks of early-stage quantum hardware.

“This work is unique, not only because it applies classical machine learning to chronic kidney disease diagnosis but also because it juxtaposes it directly alongside quantum methods under identical conditions,” explains Dr. Arslan Munir, the study’s senior author and associate professor at FAU’s Department of Electrical Engineering and Computer Science. Through this direct comparison combining two data-reduction techniques, the research provides an empirical benchmark that elucidates the current capacities of quantum-assisted diagnostics, offering clues on how quantum computing could augur new frontiers in healthcare analytics.

The research team acknowledges that advancing beyond QSVM to explore more sophisticated quantum machine learning algorithms represents a pivotal next step. Expanding experimental datasets to encompass diverse patient populations and integrating robust feature selection techniques will be essential for ensuring scalability and adaptability across various medical domains. The ultimate objective is to craft AI-powered diagnostic tools combining reliability, speed, and accessibility. Such tools could empower clinicians to make rapid, data-driven decisions, enhancing early-intervention strategies, and improving prognosis in chronic kidney disease and potentially other complex pathologies.

Dean Stella Batalama of the College of Engineering and Computer Science underscores the transformative potential of these innovations: “By synergizing machine learning with emergent quantum technologies, this research heralds a paradigm shift in early, rapid, and precise chronic kidney disease diagnosis. The healthcare community stands to benefit immensely from these advances—not only in CKD but across the spectrum of diseases where timely detection is critical.”

Florida Atlantic University’s multidisciplinary approach exemplifies the confluence of cutting-edge computer science, quantum physics, and clinical medicine. The College is recognized internationally for its trailblazing research, heavily supported by national agencies such as the National Science Foundation and the National Institutes of Health. Its commitment to pioneering degrees in artificial intelligence, data science, and cybersecurity aligns closely with the evolving demands of medical informatics and computational biology.

As quantum computing hardware continues to mature, overcoming current limitations in qubit coherence and error rates, studies like this one illuminate a roadmap for integrating quantum resources into routine clinical workflows. This fusion promises not merely incremental gains but potentially quantum leaps in diagnostic performance. With chronic kidney disease serving as a critical proving ground, the convergence of quantum machine learning and clinical diagnostics stands poised to fundamentally reshape the medical landscape, enhancing the early detection and management of complex diseases worldwide.

Subject of Research: People

Article Title: Performance analysis of classical and quantum support vector machines for diagnosis of chronic kidney disease

News Publication Date: 11-Sep-2025

Web References:
https://dx.doi.org/10.1016/j.infoh.2025.08.003
https://www.fau.edu/engineering/
https://www.fau.edu/

References:
Munir, A., et al. (2025). Performance analysis of classical and quantum support vector machines for diagnosis of chronic kidney disease. Informatics and Health. DOI: 10.1016/j.infoh.2025.08.003

Image Credits: Alex Dolce, Florida Atlantic University

Keywords: Artificial intelligence, Renal failure, Nephritis, Nephropathies, Machine learning, Quantum computing, Data analysis, Diagnostic accuracy, Medical diagnosis, Clinical medicine

Liver diseases are among the leading causes of morbidity and mortality worldwide, making effective diagnosis and prediction methods paramount. Traditional methods, while useful, may not always provide the speed or accuracy required to identify conditions like hepatitis, cirrhosis, or liver cancer at their earliest stages. This inadequacy calls for innovative solutions that can process vast amounts of data rapidly and accurately, a task ideally suited for smart technologies. Farahi and Derakhshanfard’s work meticulously outlines the various advanced methodologies that are revolutionizing this critical healthcare sector.

Artificial intelligence systems, including machine learning and deep learning algorithms, have shown great promise in medical diagnostics. By applying these techniques to liver disease, researchers can potentially analyze electronic health records, laboratory results, and even imaging data. The most significant advantage comes from the capability of these systems to incorporate a diverse range of data types, providing insights that traditional analytical methods may overlook. This multifaceted analysis can yield patterns and correlations that are vital for accurate diagnostics.

Within their review, Farahi and Derakhshanfard discuss the application of predictive analytics as a cornerstone in managing liver diseases. Predictive analytics utilizes data mining, statistics, and machine learning to identify the likelihood of future outcomes based on historical data. For instance, by analyzing previous patient histories, predictive models can gauge the risk factors associated with liver disease progression. This proactive approach enables healthcare professionals to implement preventative measures tailored to individual patients, ultimately leading to improved health outcomes.

Moreover, the review emphasizes the increasing utility of non-invasive diagnostic techniques powered by smart methods. Traditional liver diagnostic procedures often require invasive biopsies, which can be painful and carry risks. On the other hand, advancements in imaging technologies, combined with artificial intelligence, have enabled the development of alternative, non-invasive methods that are not only safer but also more cost-effective. Techniques such as elastography and enhanced liver imaging utilize AI algorithms to interpret data from imaging devices, yielding accurate liver stiffness measurements—an essential metric for diagnosing fibrosis.

Additionally, the document highlights the significance of integrating multi-omics data to enhance diagnostic accuracy. By incorporating genomics, proteomics, and metabolomics alongside traditional clinical data, healthcare providers can gain a holistic view of a patient’s liver health. This integrative approach allows for more precise, personalized treatment plans while minimizing the trial-and-error nature associated with many traditional liver disease treatments.

In discussions surrounding technology, one cannot overlook the ethical implications and challenges that accompany such advancements. Farahi and Derakhshanfard address privacy concerns related to the handling of personal health data, emphasizing the necessity for robust data governance frameworks. The use of AI in healthcare must be coupled with stringent regulations to ensure patient confidentiality and data security, thereby fostering patient trust in these pioneering methodologies.

The review also provides a thorough examination of the limitations and future directions for research in this dynamic field. Despite the promising landscape of smart methods in liver disease diagnosis, challenges remain in terms of data quality and the generalizability of AI models across diverse populations. As more data is collected, there exists an enormous potential to refine these algorithms, improving their predictive capabilities over time.

As the authors navigate through the plethora of studies in this domain, it becomes evident that collaborative efforts among healthcare professionals, data scientists, and software engineers are crucial. Interdisciplinary collaboration can lead to the development of robust AI models capable of synthesizing clinical insights with technological advancements. Such partnerships will ultimately drive the evolutionary trajectory of liver disease diagnostics forward, establishing a new standard of care.

Further exploration of patient-centered AI tools that actively engage patients in their health management can revolutionize how liver health is perceived and managed. From mobile applications that track liver health markers to online platforms that provide personalized health education based on individual risk factors, the interplay between patient engagement and smart technologies can foster a more informed patient population. Future research must also explore how to effectively implement these tools in varied clinical settings, ensuring accessibility to all patients.

In conclusion, the comprehensive review by Farahi and Derakhshanfard serves as a pivotal resource, encapsulating the rapidly evolving intersection of technology and healthcare in the context of liver disease. The continued exploration of smart methods for diagnosing and predicting liver diseases can potentially lead to actionable insights that not only advance clinical practices but also provide hope for millions affected by liver conditions worldwide. As we stand at the threshold of a new era in medical diagnostics, the text paves the way for further inquiry, collaboration, and innovation in the quest for effective liver health management.

The integration of smart technologies into the diagnostic landscape of liver diseases is just the beginning. As researchers and clinicians continue to uncover the potential of AI and machine learning, the horizon for liver disease treatment and management is bright.

With this extensive overview, Farahi and Derakhshanfard’s review resonates beyond academia, prompting a broader discussion about the future of healthcare and the vital importance of adopting cutting-edge technology for improved patient outcomes.

Subject of Research: Methods of Diagnosing and Predicting Liver Diseases Using Smart Methods

Article Title: A comprehensive review of the methods of diagnosing and predicting liver diseases using smart methods.

Article References:
Farahi, R., Derakhshanfard, N. A comprehensive review of the methods of diagnosing and predicting liver diseases using smart methods.
Discov Artif Intell 5, 230 (2025). https://doi.org/10.1007/s44163-025-00483-7

Image Credits: AI Generated

DOI: 10.1007/s44163-025-00483-7

Keywords: Artificial Intelligence, Liver Diseases, Machine Learning, Predictive Analytics, Non-invasive Diagnostics, Multi-omics Data, Patient Engagement, Healthcare Innovation.

At the heart of this book lies the fusion of theoretical innovation and clinical applicability. It details how FPGA-based (Field Programmable Gate Array) accelerators and other advanced hardware architectures are being leveraged to enhance real-time processing capabilities in medical devices, enabling instantaneous data analysis critical for timely diagnostics. This synergy between hardware and software breakthroughs is pushing the boundaries of what is achievable in personalized medicine, where patient-specific data streams can be processed with unmatched accuracy and speed.

The authors navigate through the complexities of neuromorphic computing — an emerging paradigm inspired by the human brain’s neural architecture — showcasing its potential for developing intelligent systems capable of mimicking cognitive processes for disease detection and management. Coupled with federated learning strategies that maintain data privacy by enabling collaborative model training without directly sharing patient data, these innovations represent a breakthrough in maintaining the confidentiality of sensitive health information while still harnessing the collective insights derived from distributed datasets.

Augmented reality (AR) also plays a pivotal role in this technological renaissance, offering transformative tools for both surgical procedures and medical education. Through AR-driven visualization, surgeons can access layered anatomical data during operations, improving precision and outcomes. Simultaneously, educators can employ immersive simulations to train the next generation of healthcare professionals in a risk-free, interactive environment that enhances understanding of complex physiological phenomena.

Deep learning models, with their capacity to analyze vast and multifaceted datasets, are another cornerstone discussed extensively in this publication. Their application ranges from early-stage cancer detection via image recognition to predictive analytics that anticipate patient deterioration, allowing interventions before critical events occur. The advances in algorithms are complemented by growing computational power and the increasing availability of labeled medical datasets, facilitating the development of increasingly sophisticated predictive tools.

The book does not shy away from addressing the ethical and privacy challenges that accompany such rapid technological progress. It offers thoughtful discourse on the social implications of AI-driven healthcare, emphasizing inclusivity through assistive technologies designed to bridge gaps for caregivers and patients with disabilities. Moreover, it highlights the importance of developing equitable systems that do not perpetuate existing biases embedded in training datasets, underscoring the need for transparency and accountability in AI application.

A multidisciplinary audience is clearly targeted, ranging from computer scientists immersed in algorithm development to clinicians seeking deployment of practical solutions, as well as biomedical engineers focused on device fabrication and integration. This cross-pollination of expertise is critical to the book’s overall vision: fostering a collaborative environment where technology and medicine converge to devise smarter, more efficient health systems.

One of the standout features of this text is its wealth of real-world case studies. These practical examples illustrate how hospitals and healthcare providers worldwide are implementing innovations such as IoT-enabled wearable sensors for continuous health monitoring and 5G networks that enable seamless telemedicine consultations in previously underserved communities. Such case studies illuminate both successes and challenges, providing invaluable insights into scalability, cost-effectiveness, and patient acceptance.

The editors themselves bring gravitas to the publication. Dr. Sivakumar’s expertise in bio-signal processing and wireless body sensor networks complements Prof. Dr. Shamala K. Subramaniam’s leadership in distributed computing and technological initiatives within national sports domains. Similarly, Dr. Prakasam’s prolific contributions in signal processing and wireless communication, alongside Dr. Ali Safaa Sadiq’s focus on AI and cybersecurity, lend a robust academic and practical foundation to the book’s themes.

Emerging cybersecurity concerns receive dedicated attention, reflecting the critical need to safeguard medical devices and patient information against evolving threats. Industry-funded projects led by experts like Dr. Sadiq are pushing the envelope in designing resilient, AI-integrated frameworks that can detect and mitigate cyberattacks targeting hospital networks and IoT-connected devices, ensuring system integrity and patient safety.

Notably, the work highlights how assistive technologies are enhancing inclusivity in healthcare delivery. By developing adaptive devices and interfaces, these innovations increase accessibility for individuals with a broad range of disabilities, exemplifying technology’s role in democratizing health services and reducing disparities.

The publication’s expansive view into pharmaceutical informatics, medical economics, and healthcare policy underscores the interconnectedness of technological advancement with broader social and economic systems. By analyzing the economic implications and the cost-benefit scenarios of implementing advanced computing solutions, it prepares stakeholders to make informed decisions regarding technology adoption within healthcare infrastructures.

Finally, the book serves as a visionary outlook on the trajectory of health systems, emphasizing that continuous integration of advanced computing is essential for meeting future demands in patient care and disease management. It posits that the transformative potential of AI, ML, IoT, and real-time communication networks will not only enhance diagnostic precision but also pave the way for more proactive, patient-centered approaches grounded in data-driven insights.

Advanced Computing Solutions for Healthcare is a clarion call for embracing innovation with a balanced perspective — one that champions technological prowess while conscientiously addressing ethical, privacy, and inclusivity concerns. It stands as a valuable resource for all stakeholders invested in the future of health services, revealing the profound ways in which advanced computing technologies are reshaping medicine for the better.

Subject of Research: Advanced computing technologies in healthcare including AI, machine learning, IoT, neuromorphic computing, and cybersecurity.

Article Title: Advanced Computing Solutions Revolutionizing Healthcare Delivery: Insights from Bentham Science’s Latest Publication.

News Publication Date: Not provided.

Web References: http://dx.doi.org/10.2174/97898152741341250101

Keywords: Health care, health care policy, hospice care, medical facilities, patient monitoring, pharmaceutical industry, caregivers, medical economics, health care costs

Traditional monitoring techniques employed in intravascular optical coherence tomography (OCT) are often time-intensive and unsuitable for routine clinical practice. Despite the benefits of OCT, its adoption in evaluating post-stent healing has remained limited due to the manual nature of the analyses involved. The time spent on these assessments can be burdensome for healthcare professionals, potentially leading to delayed treatment decisions and patient outcomes that suffer as a result.

In light of these challenges, a groundbreaking initiative has emerged from Helmholtz Munich in collaboration with the TUM University Hospital, introducing an innovative artificial intelligence algorithm known as DeepNeo. This advanced tool is designed to facilitate the automatic assessment of stent healing by analyzing OCT images with remarkable precision. The researchers behind DeepNeo have crafted an algorithm that performs at a level comparable to clinical experts but does so in a fraction of the time. This agility provides clinicians with timely insights necessary for managing patient care effectively.

DeepNeo operates by distinguishing between various healing patterns in vascular tissue and offers precise measurements, such as tissue thickness and stent coverage. This data is invaluable for clinicians who must monitor the post-implantation progress of their patients. The capacity of DeepNeo to deliver precise analysis means that it can significantly enhance the decision-making process within clinical settings. Its ability to operate autonomously provides another layer of utility, potentially freeing up time for healthcare professionals who manage the care of heart patients.

The researchers validated DeepNeo’s efficacy during its development, utilizing a comprehensive dataset of 1,148 OCT images from 92 patient scans. Each image was meticulously classified to delineate different types of tissue growth. Testing the algorithm in an animal model demonstrated its capability to accurately identify unhealthy tissue in 87% of cases when compared to the existing gold standard—detailed laboratory analysis. Impressively, when applied to human scans, DeepNeo maintained high precision, frequently aligning closely with expert human assessments.

Through this innovative approach, the research team highlights the potential for machine learning technologies to augment clinical workflows, allowing healthcare practitioners to make faster and more informed decisions concerning treatment paths. Dr. Carsten Marr, a key figure in this research, emphasizes the expanded role that AI tools like DeepNeo can have in enhancing the care clinicians can provide, ultimately leading to better patient outcomes.

The implications of integrating DeepNeo into clinical practice are profound. The project has garnered a Helmholtz Innovation Grant, and the team has filed for a patent to protect their innovative work. Collaborating with Ascenion, the technology transfer partner specializing in life sciences, the DeepNeo team is actively seeking industry partners for future development and implementation. Cardiologists involved in the project, PD Dr. med. Philipp Nicol and Prof. Dr. med. Michael Joner, have firmly expressed that DeepNeo facilitates a standardization in OCT imaging assessment after stent implantation, particularly crucial for improving clinical decision-making.

Their insights underline the dual benefits of this technology: not only does it promise to lessen the financial burden on the healthcare system, but it may also usher in a new era of personalized treatments tailored to individual cardiovascular needs. Their ongoing commitment to this project signifies an important step towards integrating advanced AI methods into everyday clinical practice, marking a transition to an era where technology plays a crucial role in patient care.

DeepNeo serves as a beacon of hope, showcasing how the marriage of artificial intelligence and advanced imaging techniques can transform the landscape of cardiac care. As the research progresses, the next steps involve not just refining the algorithm but also ensuring that it seamlessly fits within the existing medical frameworks. Through collaboration, ongoing research, and commitment to innovation, the integration of AI-driven solutions in monitoring stent healing can lead to significant advancements in the management of heart disease.

The potential for broader use of such technologies raises exciting prospects for future healthcare interactions, where AI could become a standard companion in clinical decision-making processes. As researchers continue to explore and optimize these advancements, the vision for an AI-enhanced healthcare system becomes an increasingly tangible reality. It is an approach that could ensure that patients receive timely care while enabling healthcare professionals to operate more efficiently and effectively in their critical roles.

The journey of DeepNeo represents a significant leap forward in the intersection of technology and medicine, illustrating the ongoing evolution of approaches to patient care and monitoring. With a focus on innovation, this pioneering research provides a template for future endeavors aimed at improving health outcomes and redefining the standards of medical care.

In summary, the advancement encapsulated by DeepNeo showcases the tremendous potential of artificial intelligence within healthcare’s most critical interfaces, particularly focusing on the nuances of post-stent healing. The implications are vast, and the commitment of the researchers ensures that we may soon see a transformational shift in how cardiovascular care is delivered. Through continued research and collaboration, the integration of AI in medicine promises a future of increased efficacy, enhanced decision-making, and improved patient care.

Subject of Research: AI-assisted assessment of stent healing using optical coherence tomography.
Article Title: DeepNeo: A Revolutionary AI Tool for Analyzing Stent Healing.
News Publication Date: [Insert Date Here]
Web References: [Insert Web References Here]
References: [Insert References Here]
Image Credits: Helmholtz Munich / Valentin Koch

Keywords

artificial intelligence, stent healing, optical coherence tomography, healthcare innovation, machine learning, cardiovascular treatment, patient management, automated assessment.

Traditional Global Positioning System (GPS) technology, while ubiquitous and reliable outdoors, fails to deliver accurate location data inside buildings due to signal attenuation and obstruction by walls and ceilings. This gap has long posed a serious challenge for healthcare applications that require precise indoor location tracking, such as fall detection, emergency response, and activity monitoring for elderly populations. The McMaster team’s BLE-based IPS presents a transformative alternative by harnessing Bluetooth signals emitted by strategically placed wireless beacons combined with wearable tags to continuously and accurately estimate a person’s location within enclosed spaces.

The core innovation lies in the system’s utilization of Bluetooth Low Energy technology, which is both energy-efficient and accessible. The IPS involves small beacons, each plugged into standard wall outlets throughout the home, emitting Bluetooth signals that are picked up by wearable tags on the individual. These signals, when analyzed in conjunction with data from motion sensors also incorporated into the system, enable the continuous tracking of user location with remarkable accuracy. The research demonstrates a 96% accuracy rate in location detection, underscoring the system’s potential for real-world deployment in dynamic residential settings.

Notably, the system is designed for practical, low-barrier implementation. It dispenses with the need for complex professional installations or detailed architectural floorplans that typically inflate costs and complicate setup. With just five beacons, the entire system can be installed for roughly $200, making it a financially viable option for widespread adoption, especially in community and home care environments catering to aging populations.

For validation, the research team installed the BLE-based IPS within two different suburban homes, including the unique McMaster Smart Home for Aging-in-Place (SHAPE) facility, a century-old house transformed into a cutting-edge research site. The system’s performance in these settings confirmed its robustness and adaptability, with the combination of Bluetooth signals and motion sensors enabling precise location detection across various rooms and living spaces even as users moved about naturally throughout the day.

The implications of this technology extend beyond eldercare. Researchers emphasize the system’s versatility for tracking both individuals and critical medical equipment in dynamic environments such as hospitals and long-term care facilities, where the ability to monitor movement in real-time can significantly improve safety protocols and streamline operations.

From a technical perspective, the BLE-based IPS employs signal strength measurements—known as Received Signal Strength Indicator (RSSI)—to estimate distance between beacons and the wearable tag. Sophisticated algorithms analyze these signals, adjusting for noise and signal variability common in residential environments, to triangulate the wearer’s position. To augment the system’s accuracy, motion sensors provide contextual data that help differentiate between stationary and active states, further refining location estimates.

This context-aware monitoring capability is poised to dramatically enhance patient safety and caregiver efficiency. Continuous mobility data can alert caregivers to prolonged inactivity—potentially signaling a fall or medical emergency—while also enabling tailored interventions that support independence and quality of life for older adults living alone.

The research team, led by Dr. Qiyin Fang of McMaster University, underscores the importance of low-cost, user-friendly design in encouraging adoption across existing homes, offering a practical path toward integrating smart technologies into aging-in-place strategies. Funding support from prominent Canadian research foundations and networks, including AGE-WELL and the Natural Science and Engineering Research Council (NSERC), has been pivotal in driving this project forward.

Looking ahead, the team envisions further enhancements to the BLE-based IPS integrating machine learning techniques to improve predictive analytics and personalization. Additional research will explore scalability across diverse housing configurations and incorporation with other smart home technologies, aiming to create comprehensive digital ecosystems that safeguard aging populations while respecting user privacy.

This breakthrough aligns with wider trends in digital health innovation, where real-time, context-aware monitoring solutions are becoming instrumental in managing chronic conditions, reducing hospitalizations, and enabling greater autonomy for vulnerable populations. The McMaster BLE-based IPS thus represents a significant step toward fulfilling the promise of technology-enabled aging-in-place.

As the global population ages, demand for scalable, reliable, and affordable mobility monitoring technologies will only intensify. This Bluetooth-based system provides a glimpse into a future where technological integration within everyday environments supports safer, healthier, and more independent lives for older adults worldwide.

For healthcare providers, policymakers, and technology developers, the McMaster team’s research offers a compelling blueprint for the development of indoor location systems that balance precision, privacy, and practicality—hallmarks necessary for transformative impact in the domain of eldercare and beyond.

Subject of Research: People

Article Title: A BLE based turnkey indoor positioning system for mobility assessment in aging-in-place settings

News Publication Date: 17-Apr-2025

Web References:
DOI link: http://dx.doi.org/10.1371/journal.pdig.0000774

References:
Wang H, Ganesh G, Zon M, Ghosh O, Siu H, Fang Q (2025) A BLE based turnkey indoor positioning system for mobility assessment in aging-in-place settings. PLOS Digit Health 4(4): e0000774.

Image Credits:
Qiyin Fang and Guha Ganesh, CC-BY 4.0

Keywords:
Bluetooth Low Energy, Indoor Positioning System, Aging-in-Place, Mobility Tracking, Elderly Care, Digital Health, Smart Home Technology, Real-time Monitoring, Healthcare Innovation, Wireless Beacons, Fall Detection, Context-Aware Systems

Coal workers’ pneumoconiosis, often known as “black lung disease,” represents a severe health hazard for miners exposed to coal dust. Traditional diagnostic methods have heavily relied on chest X-rays, which struggle to capture the nuanced and intricate lung changes associated with CWP. This limitation has hindered timely and accurate diagnosis. To overcome this, the new research pivots towards analyzing high-resolution computed tomography images, which provide more detailed views of pulmonary structures, ensuring subtle pathological changes do not go unnoticed.

The central innovation lies in leveraging a cutting-edge deep learning model called DenseNet combined with an Efficient Channel Attention Network (ECA-Net). This hybrid model exploits the intricate patterns and spatial hierarchies in HRCT images, enabling the effective distinction between different clinical manifestations of pneumoconiosis. The model was trained using an extensive dataset gathered from 217 patients with confirmed CWP and dust-exposed workers, allowing it to learn and recognize the complex imaging signatures unique to this disease.

The research team painstakingly annotated more than 1700 regions of interest (ROIs) within the HRCT images, categorizing them into four distinct clinical imaging features. These categories include small miliary opacities, nodular opacities, interstitial changes, and emphysema, each representing different pathological patterns indicative of disease severity and progression. By incorporating a robust data augmentation strategy, the researchers enhanced the dataset’s diversity, enabling the model to generalize well and maintain high performance across varying imaging scenarios.

In rigorous testing utilizing tenfold cross-validation, the DenseNet-ECA model achieved an extraordinarily high average area under the receiver operating characteristic curve (AUC) of 0.98, demonstrating exceptional discriminatory power. Remarkably, each imaging feature was classified with an AUC exceeding 0.92, underscoring the model’s consistent precision. Nodular opacities and emphysema, in particular, were identified flawlessly with AUCs of 1.0, reflecting zero classification errors in these categories.

This performance marks a significant leap forward in the computational analysis of pulmonary diseases. The integration of attention mechanisms via ECA-Net allowed the model to focus selectively on the most informative channels in the image data, thereby improving feature representation without the computational overhead typically associated with attention models. DenseNet’s densely connected pathways further enhanced gradient flow and feature reuse, which bolstered the training efficiency and accuracy of this deep learning framework.

Beyond technical prowess, the practical implications for clinical radiology are profound. Automated, reliable classification of HRCT images can provide radiologists with invaluable diagnostic support, reducing human error and diagnostic time. This is particularly critical in regions burdened with occupational lung diseases where specialist expertise may be limited. The algorithm’s ability to discern subtle imaging variations promises to improve early detection, monitor disease progression, and tailor intervention strategies more effectively.

Furthermore, the use of HRCT imaging mitigates the diagnostic ambiguity often encountered in chest X-rays, where overlapping anatomical structures obscure lung details. However, the sheer volume and complexity of HRCT images typically demand considerable time and expertise for manual interpretation. This novel algorithm addresses this bottleneck by automating classification, enhancing diagnostic throughput, and allowing clinicians to focus on patient care and treatment optimization.

While the study primarily focuses on CWP, the framework’s flexibility suggests broader applications across other interstitial lung diseases and occupational respiratory conditions. The research opens avenues for exploring similar deep learning approaches in diseases where imaging plays a pivotal role but remains challenging due to complex presentation patterns. This could spearhead a new era of AI-assisted diagnostics in pulmonary medicine.

Importantly, the researchers underline that their approach does not replace clinical judgment but rather augments it. By providing highly accurate, transparent, and reproducible assessments of HRCT features, the tool acts as a second set of eyes, enabling radiologists to confirm their impressions or identify findings that might otherwise be overlooked. This synergy between AI and human expertise exemplifies the future direction of medical imaging diagnostics.

The successful trial of this DenseNet-ECA model was registered with the Chinese Clinical Trial Registry, underscoring its methodological rigor and clinical relevance. The registration details further enhance transparency and encourage future research collaborations aimed at refining and validating the model across diverse populations and imaging modalities.

As occupational health continues to grapple with the consequences of industrial exposure, innovations such as this deep learning-based classification algorithm herald transformative progress. By harnessing artificial intelligence and high-resolution imaging, the medical community moves closer to eradicating diagnostic uncertainty, ultimately improving outcomes for countless individuals affected by coal workers’ pneumoconiosis worldwide.

This study, published in the prestigious journal BioMedical Engineering OnLine, not only exemplifies interdisciplinary collaboration between engineering and medicine but also spotlights the pivotal role of AI in addressing complex healthcare challenges. With continued development and integration into clinical workflows, such technologies promise more personalized, timely, and accurate diagnostic processes, reshaping the future landscape of pulmonary disease management.

Subject of Research: Deep learning-based classification of high-resolution computed tomography features in coal workers’ pneumoconiosis

Article Title: Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis

Article References:
Dong, H., Zhu, B., Kong, X. et al. Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis. BioMed Eng OnLine 24, 7 (2025). https://doi.org/10.1186/s12938-025-01333-4

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12938-025-01333-4