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

Artificial Intelligence in Clinical Decision-Making: A Systematic Review

Background: Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare, significantly influencing clinical decision-making processes. AI-driven systems, including machine learning algorithms, deep learning models, natural language processing, and clinical decision support systems, have demonstrated the ability to analyze vast amounts of healthcare data, identify patterns, predict outcomes, and support evidence-based clinical decisions. As healthcare systems face increasing patient complexity, workforce shortages, and demands for improved quality of care, AI offers opportunities to enhance diagnostic accuracy, treatment planning, risk prediction, and healthcare efficiency. However, concerns regarding algorithm transparency, ethical accountability, data privacy, bias, and professional acceptance continue to challenge its widespread adoption. Objectives To systematically review the evidence regarding the role of artificial intelligence in clinical decision-making across healthcare settings. To identify the benefits and effectiveness of AI-supported clinical decision-making systems in improving patient outcomes. To examine challenges, ethical considerations, and barriers associated with AI implementation in clinical decision support. To evaluate implications for healthcare professionals, particularly nurses and physicians, in AI-assisted clinical practice. Methods: This systematic review was conducted according to Joanna Briggs Institute (JBI) methodology and reported following PRISMA 2020 guidelines. Electronic databases including PubMed, Scopus, Web of Science, CINAHL, ScienceDirect, and Google Scholar were searched for peer-reviewed studies published between 2019 and 2025. The review question was structured using the PICOT framework. Methodological quality was assessed using appropriate JBI Critical Appraisal Tools. Due to heterogeneity among study designs and outcomes, findings were synthesized narratively. Results: Thirty-five studies met the inclusion criteria. Evidence demonstrated that AI-assisted clinical decision-making significantly improved diagnostic accuracy, risk stratification, treatment planning, predictive analytics, medication management, and workflow efficiency. AI systems were particularly effective in radiology, oncology, cardiology, intensive care, and chronic disease management. However, challenges related to explainability, algorithmic bias, legal liability, data privacy, workforce adaptation, and ethical governance were consistently reported. Conclusion: Artificial intelligence has substantial potential to strengthen clinical decision-making and improve healthcare outcomes. Successful integration requires robust regulatory frameworks, transparent algorithms, ethical governance, clinician training, and preservation of patient-centered care principles. AI should function as a supportive tool that enhances rather than replaces human clinical judgment.    

The role of artificial intelligence in transforming clinical practice and biomedical research: a review of opportunities and challenges

Artificial Intelligence (AI) is rapidly transforming healthcare sector by improving diagnostic accuracy, enhancing drug discovery and pharmaceutical research, and advancing electronic prescription processing (e-prescribing; eRx), although its adoption in developing contexts remains limited. This study the role of artificial intelligence in transforming clinical practice and biomedical research: a review of opportunities and challenges presents systematic review of 20 peer-reviewed articles examining the role of AI in clinical practice and biomedical research. The key findings show that AI significantly improves diagnostic precision, supports personalized medicine, enhances real-time patient management system, and optimizes medication management through intelligent e-prescribing systems with accuracy of perception of drugs for each patient. Additionally, AI contributes to faster and more efficient biomedical research processes. However, challenges such as inadequate infrastructure, high implementation costs, data privacy concerns, and limited technical expertise persist, particularly in developing country. The review concludes that effective and sustainable AI integration in healthcare requires context-specific strategies, supportive policy frameworks, increased investment in digital infrastructure, and capacity building among healthcare professionals.

Medicolegal aspects of the use of artificial intelligence in healthcare: challenges, current regulations, and future directions

ARTIFICIAL INTELLIGENCE IN MODERN MEDICINE: A COMPREHENSIVE REVIEW OF CURRENT APPLICATIONS, CHALLENGES, AND FUTURE PERSPECTIVES

Introduction: The digital transformation of healthcare is accelerating, driven by unprecedented advancements in Artificial Intelligence (AI). From large language models (LLMs) to biomolecular structure prediction, AI is redefining modern diagnostic and therapeutic standards. Aim: This review evaluates the current state of AI applications in medicine, focusing on clinical knowledge encoding, molecular drug discovery, and administrative workflow optimization, while critically addressing the technical, ethical, and systemic challenges of their institutional implementation. Materials and Methods: A structured analysis was conducted utilizing a hybrid approach that combines a multi-decade bibliometric trend perspective with a detailed synthesis of 21 landmark publications, clinical trials, and meta-analyses from high-impact journals. Results: AI demonstrates expert-level performance in medical knowledge retrieval and spatiotemporal diagnostics. AlphaFold 3 has revolutionized computational therapeutics through all-atom biomolecular interaction prediction, while ambient AI scribes significantly reduce physician burnout by automating clinical documentation workflows. However, data-driven "hallucinations" in LLMs and the inherent "black box" nature of deep learning architectures remain critical barriers to autonomous deployment. Conclusions: AI is successfully transitioning from an isolated research tool into an essential clinical "co-pilot." Achieving its full potential in Medicine 4.0 requires robust frameworks for algorithmic explainability, global dataset diversification, and a strategic synergy between machine precision and human clinical judgment.

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

This systematic review provides a comprehensive analysis of XAI methods specifically applied to tabular healthcare data for classification tasks, revealing that SHAP remains the dominant post-hoc method, achieving strong model fidelity but showing inconsistent alignment with clinical expert reasoning.

Artificial Intelligence in Healthcare Practice: Validation, Fairness, and Regulatory Challenges: A Systematic Review

Introduction Artificial intelligence (AI) is reshaping healthcare, enabled by advances in computing, affordable data storage, and the widespread adoption of electronic health records (EHRs). Machine learning (ML), deep learning (DL), and natural language processing (NLP) are increasingly used for disease diagnosis, risk prediction, and treatment planning. Objective This systematic review aimed to examine AI applications across clinical domains from 2020 to 2025, assess their diagnostic accuracy and clinical performance relative to standard practice, identify key implementation barriers including regulatory compliance, algorithmic fairness, and transparency challenges, and compare validation practices and methodological quality with earlier systematic reviews. Methods This systematic review followed PRISMA 2020 guidelines. We searched five databases (PubMed, IEEE Xplore, Web of Science, Springer, and Semantic Scholar) for studies published from January 2020 to September 2025. We included original clinical AI studies that reported prospective validation and/or external validation. Results Twenty studies met the inclusion criteria. Publication volume peaked in 2024 (n = 7, 35.0%). DL approaches were most common (n = 12, 60.0%), with convolutional neural networks (CNNs) frequently applied to medical imaging tasks. By clinical domain, 30.0% of studies focused on radiology (n = 6), 20.0% on oncology (n = 4), and 15.0% on cardiology (n = 3). For imaging-based diagnostic models, the descriptive median performance across individual studies was 0.91 AUC (no formal meta-analysis was conducted due to heterogeneity in study designs, populations, and outcome metrics). The most frequently reported challenges were regulatory compliance (55.0%, n = 11), limited algorithmic transparency (40.0%, n = 8), data quality limitations (35.0%, n = 7), and barriers to clinical integration (30.0%, n = 6). Conclusions AI demonstrates strong potential to improve the effectiveness, safety, and quality of healthcare. However, broader clinical adoption remains constrained by regulatory requirements, interpretability gaps, data quality issues, and workflow integration challenges, underscoring the need for stronger validation practices and more implementation-focused research.