This foundational work moves the conversation beyond mere performance metrics of AI algorithms. Instead, it emphasizes the dynamics of physician-AI interaction, specifically how AI can serve as a supportive tool rather than a substitute for human judgement. The team, led by Dr. Joann G. Elmore from the University of California, Los Angeles, has articulated five pivotal questions that healthcare professionals should consider when integrating AI into their diagnostic processes.

At the heart of these inquiries lies the question of information presentation. The format in which AI delivers data can significantly influence a physician’s attention and diagnostic accuracy. Will information be presented immediately, potentially fostering a biased interpretation? Or will it be available upon request, allowing for deeper engagement in the diagnostic process? Such considerations are critical for optimizing AI’s role in clinical settings.

Furthermore, understanding how AI systems arrive at their decisions can illuminate the path to more nuanced interpretations of complex medical data. Highlighting the features that were factored into AI decisions can enhance collaboration between man and machine. An effective AI model should provide ‘what-if’ scenarios that resonate with physicians’ clinical reasoning, bridging the gap between artificial intelligence and the nuanced realities of patient care.

The risks of over-reliance on AI cannot be overlooked. If physicians lean too heavily on these tools, there is a danger that they might forgo their own critical thinking processes, possibly allowing a diagnosis to slip through the cracks. The authors of the study caution that while AI can enhance accuracy, it must not replace the thorough analytical skills that physicians have honed over time. Importantly, long-term dependence on AI could lead to erosion of these vital diagnostic abilities, raising concerns about the future of healthcare as reliance on technology grows.

To deepen our understanding of AI’s impact in clinical practice, the researchers propose a series of next steps. These include evaluating different design models for AI systems within real-world clinical environments, studying the effects of AI on physician trust and decision-making, and monitoring the development of clinical skills in environments utilizing AI. Such rigorous assessments will provide insights that can help refine AI technologies, ensuring they are equipped to complement the medical expertise of healthcare providers rather than supplant it.

Moreover, it is essential for AI systems to feature adaptive algorithms that adjust assistance based on individual physician needs. This approach can help maximize both the effectiveness of diagnostics and the retention of essential clinical skills among physicians. By tailoring AI support to suit the context of each case, practitioners can benefit from AI without compromising their role in the diagnostic process.

As the conversation around AI’s role in healthcare expands, it becomes evident that a thoughtful approach is paramount. Elmore states, “AI holds immense potential for enhancing patient care, yet improper integration could inadvertently lower the quality of healthcare.” Highlighting human factors such as timing, trust, and skill maintenance will be critical in steering the successful adoption of AI technologies.

As we look ahead, it is clear that the relationship between AI and healthcare is a complex interplay that warrants ongoing exploration. The framework proposed by Elmore and her team serves not only to guide the design and implementation of AI tools but also emphasizes the importance of collaboration between technology and healthcare professionals. It is vital to ensure that AI systems are designed with the understanding that they are there to assist, not replace, the human touch in diagnostics.

In a landscape where technological advancements are occurring at breakneck speed, maintaining a focus on the symbiotic relationship between AI and medical expertise will pave the way for safer and more effective healthcare solutions. The continuing dialogue between researchers, clinicians, and technologists will be essential as their collective insights drive improvements in clinical practice and ultimately lead to better outcomes for patients.

As we stand on the brink of a new era in medicine, the insights offered by this research remind us that the human element remains irreplaceable. AI offers a powerful set of tools, but the art of diagnosis is a uniquely human skill that must be nurtured and preserved. By thoughtfully integrating AI into patient care strategies, we can unlock the full potential of both artificial and human intelligence, ensuring a future where healthcare is not only efficient but also profoundly humane.

Subject of Research: People
Article Title: Artificial intelligence and computer-aided diagnosis in diagnostic decisions: 5 questions for medical informatics and human-computer interface research
News Publication Date: 17-Oct-2025
Web References: Link to Article
References: Journal of the American Medical Informatics Association
Image Credits: Not applicable

Keywords

Artificial intelligence, machine learning, medical technology, health care delivery, adaptive systems

One of the most promising applications of AI in outpatient primary care is its potential to enhance diagnostic accuracy and speed. Traditional diagnostic procedures can often be time-consuming and subject to human error. Employing AI algorithms trained on vast data sets allows for remarkably quick analysis of symptoms and relevant patient history. This, in turn, could lead to improved outcomes as clinicians receive actionable insights at unprecedented speed. With algorithms that can learn and adapt, the potential for AI to outstrip human capability in pattern recognition and decision-making is increasingly within reach.

AI also holds the key to optimizing patient triage and management. In outpatient settings, where the volume of patients can lead to long waiting times, intelligent algorithms can prioritize cases based on urgency. By analyzing a patient’s symptoms and history, AI can recommend the most appropriate care pathway. This not only ensures that those in need of immediate attention receive it but also streamlines services, leading to a more efficient healthcare system. With a well-designed AI triage system, the patient experience can significantly improve and healthcare costs can potentially decrease.

Moreover, the integration of AI in outpatient care brings forth a wealth of data that can enhance healthcare providers’ understanding of disease dynamics within populations. This predictive analytics capability can lead to timely interventions, thereby reducing instances of advanced disease conditions. Data drawn from AI systems can also be employed to track chronic conditions more effectively, allowing for early identification of health deteriorations. Thus, the ultimate goal of medical professionals—to promote wellness and prevent disease—is supported through AI’s intricate data analysis capabilities.

Despite the burgeoning potential of AI, there are considerable challenges that need to be addressed. One such challenge is the issue of data privacy and security. The healthcare sector is one of the most sensitive industries when it comes to personal data, and the thought of AI systems handling this information raises ethical concerns. Patients must have assurance that their data will be handled securely and ethically. Additionally, the systems used must comply with regulations, which can vary significantly from one region to another. Addressing these concerns is imperative for the successful integration of AI into outpatient primary care.

Furthermore, the problem of algorithmic bias presents another obstacle. AI systems are only as effective as the data they are trained on. If the training data is skewed or not inclusive of diverse population groups, the algorithms may produce incorrect or harmful outputs. Misdiagnoses could escalate health disparities rather than mitigate them. Therefore, researchers and developers must prioritize the creation of inclusive datasets and algorithms that are tested across diverse demographic groups to minimize biases. Only then can AI be regarded as a truly equitable tool in healthcare.

Training healthcare professionals to understand and interpret AI-generated recommendations is equally vital. As the technology evolves, so must the capabilities of the practitioners who rely on it. Continuous education and professional development programs will be necessary to ensure that clinicians are well-equipped to integrate AI into their practice. Understanding the strengths and limitations of AI technologies will foster better collaboration between healthcare providers and AI systems, leading to improved patient outcomes.

The future direction of AI in outpatient primary care will inevitably involve the creation of more user-friendly interfaces between medical professionals and AI systems. Simplifying interactions while ensuring that the complexity of the algorithms is preserved will be essential. Healthcare professionals should not be overwhelmed by technical jargon or complex data outputs. Instead, the goal should be to create intuitive platforms that present information in a straightforward manner, enabling clinicians to make informed decisions efficiently.

Collaboration among various stakeholders is crucial for the successful deployment of AI in outpatient settings. This includes partnerships among tech developers, healthcare institutions, regulatory bodies, and community leaders. By fostering an environment of cooperation and shared knowledge, best practices can be established, paving the way for a more robust integration of AI into healthcare systems. Such collaborations can lead to innovative solutions addressing real-world problems faced in outpatient primary care.

Investigating the long-term implications of AI’s role in outpatient primary care is another avenue worth exploring. This involves studying how AI affects clinician-patient relationships, care outcomes, and the overall healthcare ecosystem. Will AI systems reinforce existing practices, or will they disrupt established norms in healthcare delivery? Research in this area will provide valuable insights into the sustainability of AI technologies within outpatient care frameworks.

A focus on ethical AI development should also be a priority. The increasing adoption of AI must be guided by ethical considerations that put patient welfare at the forefront. This includes ensuring informed consent from patients regarding the use of AI in their care. Healthcare providers should engage in discussions about how AI systems can be utilized responsibly while maintaining trust within patient-provider relationships. Ethical frameworks must be established, allowing for transparency and accountability in AI applications.

The scoping review by Iannone, Kaur, and Johnson captures the transformative potential of AI in outpatient primary care while confronting the multifaceted challenges it presents. The insights garnered from their exploration serve not merely as an academic exercise but as critical reflections on the future of healthcare. As AI technologies continue to advance, so too does the responsibility of the healthcare community to approach these developments thoughtfully and inclusively.

In conclusion, artificial intelligence holds the promise of revolutionizing outpatient primary care by enhancing diagnostic accuracy, improving patient management, and offering predictive analytics capabilities. However, this revolution is not without its challenges—data privacy, algorithmic bias, and the need for continuous professional development are all hurdles that must be surmounted. The future of healthcare will depend on collaborative efforts, ethical considerations, and a commitment to inclusivity in AI development. Only through these pathways can we hope to harness AI’s potential for good, ushering in an era of improved health outcomes and enhanced patient experiences.

Subject of Research: Artificial Intelligence in Outpatient Primary Care

Article Title: Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions

Article References:

Iannone, S., Kaur, A. & Johnson, K.B. Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions.
J GEN INTERN MED (2025). https://doi.org/10.1007/s11606-025-09938-0

Image Credits: AI Generated

DOI: 10.1007/s11606-025-09938-0

Keywords: Artificial intelligence, outpatient care, healthcare technology, diagnostic accuracy, algorithmic bias, patient management

Augmented intelligence aims to complement the capabilities of healthcare professionals by offering tools that aid in diagnosis, treatment planning, and patient management. By leveraging vast datasets and applying sophisticated algorithms, augmented intelligence systems can analyze data far more quickly and accurately than the human mind alone. This revelation opens new avenues for enhancing decision-making in medical practices. However, the challenge remains: can we ensure that these technological marvels enhance rather than undermine the ethical fabric of medicine?

At the heart of this dialogue is the concept of trust. Healthcare relies heavily on the trust built between patients and practitioners, a bond that could be jeopardized if patients perceive technology as a hindrance rather than a help. To preserve this vital trust, it is essential that augmented intelligence systems are developed transparently and are tailored to enhance human capabilities rather than replace them. The authors stress that technology should work in concert with medical professionals, ensuring that the human element of care remains central to the healing process.

Moreover, patient safety cannot be compromised in the race to implement advanced technologies. The deployment of augmented intelligence tools must come with comprehensive testing and rigorous validation procedures. This means not just running algorithms against data but understanding their implications and how they interact with clinical practice. Errors in medical decisions fueled by faulty AI systems can have catastrophic outcomes, so proactive measures to prevent such situations are paramount.

Furthermore, the issue of bias in AI systems cannot be overlooked. Data used to train AI models is often drawn from historical medical records, which can embed societal biases and inequalities within its parameters. Thus, it is imperative that researchers conduct thorough evaluations of the input data to ensure that the augmented intelligence systems serve diverse patient populations equitably. A failure to address this concern runs the risk of exacerbating existing disparities in healthcare access and outcomes, further alienating already marginalized groups.

There’s a pressing need for a multidisciplinary approach to integrating augmented intelligence into medicine. The collaboration of physicians, data scientists, ethicists, patients, and policymakers can pave the way for innovative solutions that are both ethical and effective. This coalition can help shape the legal and social frameworks that govern the use of these technologies in healthcare settings, ensuring accountability and a commitment to patient-centered care.

As we look toward future advancements, one of the most exciting prospects of augmented intelligence is its ability to enhance predictive analytics. Imagine a world where doctors can foresee potential health crises before they occur, allowing for preventative measures that save lives and reduce healthcare costs significantly. However, the realization of this vision demands rigorous validation studies and ethical oversight to navigate the risks involved in predictive systems. The fine line between proactive care and intrusive surveillance must be respected, maintaining patient autonomy and consent as guiding principles.

Another fascinating area of exploration is the potential for augmented intelligence to improve medical education. By providing personalized learning experiences and real-time feedback, AI tools can help train the next generation of healthcare providers in a manner that enhances their diagnostic skills and clinical judgment. However, the authors caution that reliance on machines for learning can lead to complacency. Balancing the use of technology with traditional educational methods will be crucial in crafting skilled and competent healthcare practitioners.

Furthermore, in the realm of patient engagement, augmented intelligence can transform the way individuals interact with their health information. By presenting complex data in an understandable format, these technologies can empower patients to make informed decisions about their care. The desire to incorporate patient perspectives into care planning must be at the forefront of any initiative that seeks to integrate AI into healthcare.

Nonetheless, as we embrace these cutting-edge advancements, we must also remain vigilant against potential pitfalls. Ethical considerations should be woven into the fabric of technology development from the outset. This includes not only the mechanisms by which data is collected and analyzed but also how decisions made by augmented intelligence systems can be communicated to both healthcare providers and patients. Transparency in these processes will strengthen trust and facilitate greater acceptance of AI in the medical community.

In conclusion, the promise of augmented intelligence in medicine is both profound and multifaceted, offering a glimpse into a future where technology elevates rather than diminishes the human experience in healthcare. Striking the right balance will be critical, ensuring that as we forge ahead into an era of unprecedented technological innovation, we do so with an unwavering commitment to the ethical principles that define the medical profession. The insights of Idan, Celi, and Einav serve as a clarion call for continued dialogue, collaboration, and careful consideration of how augmented intelligence can be leveraged for good in medicine. By fostering a culture of robust debate and exploration, we may yet achieve a harmonious integration of technology and care that truly benefits all.

Subject of Research: Augmented intelligence in medicine and its ethical implications.

Article Title: Augmented intelligence should be good for medicine, if medicine is to remain good for us.

Article References:

Idan, D., Celi, L.A., Einav, S. et al. Augmented intelligence should be good for medicine, if medicine is to remain good for us.
Discov Artif Intell 5, 235 (2025). https://doi.org/10.1007/s44163-025-00256-2

Image Credits: AI Generated

DOI: 10.1007/s44163-025-00256-2

Keywords: Augmented intelligence, healthcare, ethics, patient care, artificial intelligence.

Amid this turmoil, researchers and healthcare professionals began exploring innovative solutions to address the escalating strain on ICUs. One avenue of hope has emerged in the form of artificial intelligence (AI). According to Indranil Bardhan, a prominent professor at the Texas McCombs School of Business, AI technologies possess the potential to revolutionize hospital operations by forecasting the length of time patients can expect to stay in intensive care. With this predictive capability, hospitals could potentially optimize bed occupancy and improve operational efficiencies, all while striving to reduce costs associated with prolonged patient care.

However, the application of AI in medical settings is not without its challenges. While mechanisms exist to accurately predict patient outcomes and length of stay, a significant barrier remains: a lack of interpretability in these predictive outcomes. Bardhan emphasizes that healthcare providers often view AI with skepticism when they cannot comprehend the underlying reasons for its predictions. This lack of transparency can hinder acceptance among medical practitioners who require detailed explanations to trust and utilize these technological advancements in their decision-making processes.

To tackle this issue, Bardhan and his team have undertaken the development of an innovative approach known as explainable artificial intelligence (XAI). By focusing on creating models that not only deliver predictions but also elucidate the rationale behind them, the researchers aim to enhance the integrity and usability of AI systems in clinical settings. Collaborating with McCombs doctoral student Tianjian Guo, Ying Ding from the School of Information at the University of Texas, and Shichang Zhang from Harvard University, they designed a sophisticated model that draws insights from a substantial dataset comprising over 22,000 medical records collected between 2001 and 2012.

The essence of the AI model lies in its ability to analyze a comprehensive set of 47 distinct patient attributes at the time of admission, including crucial factors such as age, gender, vital signs, medications, and diagnoses. By processing this data, the model constructs probabilistic graphs that illustrate the likelihood of a patient being discharged from the ICU within seven days. Notably, the model highlights which specific attributes carry the most weight in influencing a patient’s outcome and delineates the interactions among those factors, providing a holistic view that enhances understanding.

An illustrative case demonstrates the model’s functionality: for a patient with a respiratory system diagnosis, the prediction indicated an 8.5% chance of discharge within a week. The model not only identified the primary factor leading to this conclusion but also contextualized secondary influences such as the patient’s age and medical history. This layer of explanation empowers healthcare practitioners to appreciate and leverage AI predictions more effectively, ultimately leading to enhanced resource allocation in ICUs.

Testing the practicality of their model included surveying six physicians practicing in the Austin area who specialize in critical care. The physicians were presented with samples of the model’s explanatory outputs for evaluation. Remarkably, four of the six clinicians expressed confidence that the model could serve as a valuable tool in optimizing staffing levels and resource management. They envisioned utilizing the insights provided by the model to inform patient scheduling, marking a significant step toward integrating AI into daily clinical workflows.

A potential limitation of the developed model, however, is the age of the training data, particularly given the transition in 2014 from the ICD-9-CM medical coding system to the more detailed ICD-10-CM. This change significantly enhances the granularity of diagnostic coding and classification, suggesting that the model could benefit from updated datasets. Bardhan highlights the importance of access to more contemporary medical records to refine and improve their predictive capabilities, fostering enhanced accuracy and relevance in real-world applications.

Nevertheless, the versatility of the model exhibits promise that extends beyond traditional adult ICUs. Bardhan notes that, with further adjustments, the model could potentially be adapted for use in pediatric and neonatal ICUs, as well as in emergency room contexts. This adaptability underscores a broader utility, as healthcare providers across multiple settings face similar challenges in predicting patient bed needs and optimally managing hospital resources.

The implications of explainable AI modeling not only touch upon operational efficiencies but also resonate in the ongoing effort to elevate the quality of patient care in modern healthcare systems. By marrying advanced machine learning techniques with a focus on interpretability, researchers like Bardhan are striving to build a bridge between technological innovation and practical applicability in hospitals worldwide. Their work signifies a step forward in making AI a trusted ally for healthcare professionals, ultimately aiming to foster better patient outcomes while maintaining the delicate balance of resource management that lies at the heart of effective medical care.

1. As the medical field continues to embrace technological advancements, the integration of explainable AI could also spark meaningful discussions around ethics and accountability. The capacity to dissect and clarify AI-generated predictions prompts necessary conversations about the role of technology in decision-making processes and the human elements that remain integral to patient care, laying the groundwork for future exploration in this rapidly evolving domain.

2. The intersection of AI and healthcare presents rich opportunities for further inquiry and innovation, as researchers and clinicians alike endeavor to harness these powerful tools while preserving the core values of empathy and understanding that define patient care around the globe. Through ongoing dialogue and collaboration, the healthcare community can embrace the evolving landscape of artificial intelligence, paving the way toward an era of enhanced medical capabilities that prioritize clarity, trust, and patient-centered outcomes.

3. With ongoing research and refinement, the future of AI applications in healthcare appears bright, signaling a movement toward more intelligent, interpretable, and ultimately more effective healthcare solutions. Upholding transparency remains central to fostering the acceptance of these advanced technologies, as they become increasingly integrated into the daily fabric of medical practice.

4. Efforts like those of Bardhan and his colleagues illuminate the path forward, as they establish the groundwork for future innovations that will not only advance medical science but also seek to redefine the relationship between technology and patient care as it has been known historically.

5. In sum, the fundamental question surrounding the relationship between AI algorithms and their human counterparts boils down to trust. As healthcare systems progress, the role of explainable AI will prove crucial—not just in predicting outcomes but in nurturing confidence among physicians and clinicians, allowing for informed decision-making that extends a hand to each patient’s unique circumstances.

As we look toward a future where technology and humanity intermingle ever more closely, the convergence of explainable AI with clinical practice may very well be one of the key catalysts for transforming the landscape of healthcare delivery.

Subject of Research: Explainable AI in Intensive Care Unit Length of Stay Predictions
Article Title: An Explainable Artificial Intelligence Approach Using Graph Learning to Predict Intensive Care Unit Length of Stay
News Publication Date: 11-Dec-2024
Web References: AI in ICU Prediction, Research Paper
References: Research by Indranil Bardhan and colleagues
Image Credits: None provided

Keywords

AI, ICU Length of Stay, Explainable Artificial Intelligence, Healthcare Management, Patient Care, Machine Learning

Le’s work reflects a growing consensus within the medical community that understanding the decision-making processes behind AI is vital for its integration into healthcare. Current AI systems are often likened to "black boxes," where the rationale behind predictions is opaque even to their developers. This lack of transparency can breed skepticism among medical practitioners and patients, and it deserves scrutiny as AI continues to evolve in complexities and applications. The parallel between understanding an automated diagnosis and engaging with a health expert is illuminated by Le’s research, where clear lines of reasoning significantly bolster trust in AI systems.

In an innovative leap, Le and her colleagues have developed ItpCtrl-AI, a framework that marries interpretability with accuracy in the realm of chest X-ray interpretation. This tool, which stands for interpretable and controllable artificial intelligence, has the potential to transform diagnostic practices by not only providing results but also elucidating the basis for those results. This is achieved through an intricate system where the AI is trained to emulate the observational habits of radiologists. By meticulously tracking where radiologists focus their gaze and the duration of time spent on different regions of a chest X-ray, the researchers were able to create a "heat map." The heat map provides visual representation of areas that warrant more scrutiny versus those that require less attention, offering insights that conventional AI systems may often overlook.

The strength of ItpCtrl-AI lies not only in its accuracy but in its transparency. The framework elucidates the AI’s reasoning process, making it indispensable for medical professionals who rely on accuracy and consistency in their assessments. This heightened transparency is particularly compelling in a medical context, where understanding the underlying decision-making logic is crucial for the acceptance of AI-driven diagnoses. As Le points out, when physicians comprehend why a diagnosis was rendered, their ability to place trust in the AI augments significantly. Therein lies an essential component of successful AI integration into clinical settings, which often hinges on perceived reliability and the overall concordance with established medical knowledge.

Moreover, the accountability that comes with a transparent AI framework is paramount, particularly in high-stakes domains such as healthcare. Medical practitioners are expected to take responsibility for their diagnoses, and the use of AI should not diminish this ethical obligation. Le’s methodology facilitates this accountability. When physicians utilize ItpCtrl-AI in their practice, they step into a role where they can trace back the AI’s reasoning to ensure it aligns with their own medical expertise and judgment. This synergy between human and machine is what will define the future of diagnostic medicine.

Additionally, the ethical questions surrounding AI decision-making cannot be ignored. As machines increasingly assume roles traditionally held by healthcare professionals, the demand for fairness and equity in AI diagnosis becomes more pronounced. Le argues that if the mechanics behind an AI system’s decision-making are opaque, it becomes difficult to ascertain whether those decisions are in harmony with societal values. This raises the question of bias—both in the datasets used to train AI systems and in the resulting algorithms. With a transparent framework such as ItpCtrl-AI, these concerns can be addressed more effectively, fostering a culture of responsible AI use in medicine.

Adding to the momentum of her research, Le, along with her team, is currently delving into the applicability of ItpCtrl-AI for interpreting more complex imaging such as three-dimensional CT scans. This subsequent phase of research promises to usher in advancements that could redefine the operational realities of diagnostic imaging. The collaboration with the MD Anderson Cancer Center in Houston is particularly promising, as it provides an essential avenue for testing and refining ItpCtrl-AI on various imaging modalities, which will further enhance its capability to support clinicians in their decision-making process.

In the forthcoming publication titled “ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions” in the prestigious journal Artificial Intelligence in Medicine, Le and her research team detail the intricacies of this transformative approach. The paper fortifies the notion that interpretability is not an optional feature of AI systems in healthcare, but a fundamental principle that underpins successful implementation.

The push for utilizing AI in healthcare is not merely a call for innovation; it is a demand for a responsible, ethical, and transparent integration into clinical environments. As the technology continues to burgeon, the discourse surrounding the ethics and efficacy of AI systems like ItpCtrl-AI is imperative. The need for an AI that not only predicts but also elucidates its reasoning reflects a significant stride toward the future of medical diagnostics, enhancing patient safety, and ultimately shaping a new standard for accuracy in radiology.

As healthcare adopts these advanced technologies, the importance of interdisciplinary collaboration among computer scientists, radiologists, and ethicists cannot be overstated. Through partnerships and shared vision, the medical community can work to ensure that AI-enabled solutions serve to enhance the human capacity for empathy and understanding in patient care. The future of healthcare will not merely be dictated by the algorithms we deploy but by how responsibly we incorporate these technological advancements into our ethical frameworks.

In conclusion, Ngan Le’s research on ItpCtrl-AI encompasses the complexities of AI in healthcare while championing the highly sought attribute of transparency. As her work progresses, it promises to bridge the gap between machine intelligence and human comprehension, fostering a healthcare environment poised to trust and effectively utilize AI’s capabilities.

Subject of Research: AI interpretation of chest X-rays
Article Title: ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions
News Publication Date: 12-Dec-2024
Web References: http://dx.doi.org/10.1016/j.artmed.2024.103054
References: Artificial Intelligence in Medicine
Image Credits: Russell Cothren

Keywords: Artificial intelligence, Radiology, Machine learning, Medical ethics, Medical technology, Machine ethics, Social ethics