The team applied the PRISMA framework, the gold-standard protocol for systematic reviews, to sift through the scientific record. Beginning with 1,860 records drawn from major databases including PubMed, IEEE Xplore, Scopus, ScienceDirect, ACM, Springer and Wiley, they removed 657 duplicates and screened 1,203 titles and abstracts. After full-text eligibility assessment of 331 articles, 108 studies were ultimately included in the qualitative and quantitative synthesis. The review focused on literature published between 2021 and 2026, capturing the most recent wave of AI-driven healthcare innovation. Each selected study was then evaluated using the Technology Readiness Level framework, a nine-stage maturity scale originally developed by NASA, to determine how close each technology actually is to real-world deployment.

The TRL analysis produced one of the review’s most consequential findings. The majority of healthcare AI technologies cluster between TRL 3 and TRL 5, meaning they exist as conceptual frameworks, proof-of-concept demonstrations, laboratory prototypes or early tests in relevant environments. Very few studies demonstrated large-scale clinical implementation or fully operational deployment, which would correspond to TRL 7 through 9. In practical terms, the review shows that healthcare AI remains largely in transition from research-based proof-of-concept systems to genuine clinical applications, with TRL 4 and TRL 5 being the most common maturity levels observed across the literature.

Technically, the review organizes the field into a hierarchical taxonomy of AI methods and their healthcare applications. Machine learning, the older sibling of the AI family, develops data-analysis algorithms that extract features from data and improve with exposure to more examples. Techniques such as Support Vector Machines and Naïve Bayes classifiers are already being used to classify facial expressions for patient monitoring and disease diagnosis. Deep learning goes further, employing artificial neural networks with multiple hidden layers and millions or even billions of parameters. These architectures have proven so powerful in medical imaging that systems such as Google’s DeepMind and IBM’s Watson have demonstrated performance on malignant tumor detection that rivals or exceeds human radiologists, according to studies cited in the review.

The application landscape the authors map is remarkably broad. Medical image analysis, transformed by deep learning, underpins modern diagnosis, treatment and monitoring across radiology, pathology, dermatology and ophthalmology. Disease prediction and risk assessment models help clinicians anticipate dangers, identify lesion locations and reduce medical errors. Automated screening systems are accelerating diagnostics through predictive analytics, medical imaging and clinical decision support. Beyond diagnosis, AI is reshaping treatment planning and patient care, detecting healthcare insurance fraud through blockchain-empowered analytics, powering patient engagement tools that generate personalized insights, and enabling preventive care through predictive models that flag disease risks before symptoms appear. Subdomains such as robotic surgery, virtual health aides, drug discovery and remote patient monitoring round out the picture of a technology touching nearly every corner of medicine.

The review also highlights how AI is converging with other emerging technologies. The Internet of Things connects wearable devices and sensors that stream continuous patient data, enabling real-time telehealth, remote diagnosis and even remote surgery when paired with 5G networks. Blockchain, the decentralized and immutable ledger technology originally conceived for Bitcoin, offers integrity, traceability and non-repudiation for electronic medical records, securing data sharing across institutions through hash chains, digital signatures and consensus mechanisms. The authors point to studies combining AI and blockchain for secure health record management and patient identity systems, as well as digital twin technologies that create patient-specific computational models for personalized medicine, including neurosymbolic digital twins for cardiovascular disease prediction.

Yet the challenges catalogued in the review are formidable. Data collection remains a fundamental bottleneck: patient confidentiality concerns limit the availability of relevant information, and privacy regulations such as GDPR, while essential for protecting personal data, complicate research collaboration. Data quality problems, including inconsistent records, directly degrade algorithm performance. On the algorithmic side, bias in training data can distort AI outcomes, and overfitting causes models to latch onto irrelevant correlations. The notorious black-box problem, in which deep learning systems reach conclusions that even their creators cannot fully explain, undermines clinical trust and accountability. When a physician cannot understand why an AI system recommends a treatment, the reliability of medical advice itself comes into question.

Ethical and social concerns compound the technical ones. Accountability for AI errors is difficult to assign when decision-making is opaque, and universal ethical standards for healthcare AI have yet to be established, although regulatory bodies such as the FDA are developing assessment frameworks. Fear of job displacement fuels skepticism among healthcare workers, and the authors argue that roles must be transformed rather than eliminated to allow AI advancement. Clinical implementation poses its own barriers: most AI research has not been developed within actual clinical settings, generalization to diverse patient populations is complicated by small or biased training datasets, and successful adoption requires stakeholder engagement, workflow integration that does not disrupt care, and training for healthcare personnel. The review also flags that mental health, chronic disease and elder care remain understudied areas, and that integration of AI with IoT and blockchain is still restricted, leaving fertile ground for future research.

The authors chart a forward agenda that reads like a roadmap for the next decade of medical AI. Clinical validation and real-world deployment emerge as the most critical research needs, given that most current studies sit at intermediate readiness levels. Future work should prioritize explainable AI models that enhance transparency and clinician trust, federated learning that enables privacy-preserving collaboration across institutions, and large language models and generative AI for clinical decision support, medical knowledge extraction and synthetic data generation. Digital twin technologies could personalize care at the level of the individual patient, while stronger regulatory frameworks and extensive clinical validation trials are needed to guarantee safe, effective and sustainable integration. The review’s ultimate message is one of measured optimism: AI-based systems have already delivered marked improvements in diagnostic precision, tailored treatment strategies and healthcare efficiency, but building trustworthy, scalable and clinically applicable systems will require the field to close the gap between what works in the laboratory and what works at the bedside.

Subject of Research: A PRISMA-based systematic review of artificial intelligence applications, techniques and challenges in healthcare

Article Title: A PRISMA-based Systematic Review of Artificial Intelligence in Healthcare, its Applications and Challenges

Article References: Yadav, D., Yadav, P., & Yadav, H. (2026). A PRISMA-based Systematic Review of Artificial Intelligence in Healthcare, its Applications and Challenges. Discover Informatics, 1(1), Article 12. https://doi.org/10.1007/s44564-026-00009-y

Image Credits: AI Generated

DOI: 10.1007/s44564-026-00009-y

Keywords: Artificial Intelligence, Healthcare, Machine Learning, Deep Learning, IoT, Blockchain, Technology Readiness Level, PRISMA, Medical Imaging, Explainable AI, Data Privacy, Algorithmic Bias

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