Generative AI in Healthcare: Applications and Challenges

Large Language Models in Medicine: Opportunities, Limitations, and Future Directions – A Scoping Review

A scoping review of 24 PubMed-indexed studies published between 2023 and 2026 was conducted to assess current applications, benefits, limitations, and future directions of LLMs in healthcare.

Explainable Artificial Intelligence for Tabular Data in Healthcare: A Systematic Review of Methods, Evaluation, and Applications

Explainable Artificial Intelligence (XAI) has emerged as a critical enabler for the adoption of machine learning models in high-stakes domains such as healthcare. While significant progress has been made in XAI for computer vision and natural language processing, tabular data—the predominant format of electronic health records—presents unique challenges and opportunities. This systematic review provides a comprehensive analysis of XAI methods specifically applied to tabular healthcare data for classification tasks. We examine 21 primary studies published between 2020 and the first half of 2026, covering three complementary perspectives: (1) intrinsically interpretable models, (2) post-hoc methods including LIME, SHAP, and their variants, and (3) evaluation frameworks that assess both model-centered fidelity and human-centered clinical alignment. Our analysis reveals that SHAP remains the dominant post-hoc method, achieving strong model fidelity but showing inconsistent alignment with clinical expert reasoning. Key findings include the significant impact of class imbalance on explanation consistency, the importance of clinician-centered evaluation, and the emergence of hybrid approaches integrating XAI with generative AI and transfer learning. We identify critical gaps, including limited adoption of XAI in AutoML pipelines, lack of standardized evaluation metrics, and predominance of single-institution validation studies.

Exploring Arti-icial Intelligence in Healthcare: A Review

Bridging gaps in health artificial intelligence: challenges in MDPI research articles

Technological advancements in artificial intelligence (AI) have transformed healthcare by improving early disease detection, personalized treatment, predictive analytics, and clinical decision support systems. However, AI adoption in healthcare faces critical challenges, including data privacy concerns, algorithmic bias, regulatory barriers, usability issues, and system interoperability. Addressing these issues requires standardized regulations, ethical frameworks, and interdisciplinary collaboration to ensure responsible AI integration. This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer. The review focuses on Multidisciplinary Digital Publishing Institute (MDPI) journal articles to identify key contributors, emerging trends, and research gaps in AI-driven healthcare. Findings highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics, while exposing persistent challenges such as a lack of standardized AI models, ethical concerns, and accessibility disparities. By mapping the research landscape, this study provides evidence-based insights and recommendations to address AI adoption barriers, improve transparency, and guide future research in healthcare AI. The results contribute to developing a more equitable, efficient, and trustworthy AI-driven healthcare system.

Enhancing Clinical Decision-Making Using Generative AI-Powered Knowledge Retrieval Systems: A Review of Emerging Approaches and Challenges

A wealth of biomedical information and literature, complex electronic health records (EHRs), disjointed guidance, and time pressures associated with the care process are all affecting clinical decision-making. The objective of this review was to discuss the potential of generative AI-driven knowledge retrieval systems for clinical decision-making and to describe some of the technical, compliance, ethical, and implementation challenges and limitations. This purposively selected, 42-source structured narrative review with scoping review elements was conducted based on publications retrieved from PubMed, Scopus, IEEE Xplore, Google Scholar, WHO/FDA, Web of Science, and major clinical informatics journals from 2020-2026. Study results demonstrate that retrieval-augmented generation (RAG) systems can provide accurate and relevant information by grounding content generated by large language models (LLMs) in clinical guidelines, biomedical literature, drug databases, EHR data, and institutional protocols. The results showed that RAG systems yielded better biomedical system performance than the baseline LLM, with an OR of 1.35 (95% CI: 1.19, 1.53). There are potential impacts, however, such as hallucinations, incomplete retrieval, incomplete and comprehensive datasets, privacy breaches, and lack of multilingual validation. The study concludes that evidence-based, auditable, locally adaptable, and supervised by licensed clinician retrieval systems with generative AI can support safer, faster, and more relevant decision-making processes in clinical settings. Future studies should involve prospective multi-site implementation, a clear retrieval pipeline, multilingual datasets, EHR integration, ongoing monitoring, and clinical governance models to ensure safe use.

AI-Enabled Smart Healthcare Systems: Innovative Approaches to Disease Prediction, Accurate Diagnosis, and Effective Public Health Management

Artificial intelligence (AI) has moved from a peripheral research interest to a central component of contemporary healthcare delivery, with deep learning systems now matching or exceeding specialist-level performance on discrete diagnostic tasks spanning dermatology, ophthalmology, radiology, and oncology. This paper reviews the technical foundations and applied evidence base for AI-driven smart healthcare systems across three domains: disease prediction from structured and unstructured clinical data, image-based diagnostic classification, and population-level public health management, including the accelerated adoption of AI tools during the COVID-19 pandemic. The review synthesizes landmark diagnostic-performance studies, including dermatologist-level skin cancer classification, diabetic retinopathy detection, pneumonia detection from chest radiographs, and international breast cancer screening evaluation, alongside the clinical machine learning literature addressing implementation, validation, and algorithmic bias. Comparative tables summarize reported diagnostic performance metrics, data modalities, and clinical domains across the reviewed systems. The paper concludes that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begun to resolve, and identifies prospective real-world validation and equity-focused model auditing as the central future research prospects.