Ethical considerations for multimodal artificial intelligence in healthcare

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

Artificial intelligence in medicine: An ethical and legal review.

This review addresses the primary ethical principles relevant to AI in medicine - including respect for patient autonomy, beneficence, non-maleficence, and justice - alongside key legal frameworks with respect to liability, data protection, regulatory compliance, and algorithmic transparency.

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

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 to highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics.

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.

Generative AI in Healthcare: Applications and Challenges

Generative AI demonstrably accelerates diagnostic workflows, augments scarce clinical datasets, personalizes communication, and supports discovery pipelines, and the paper concludes with a translational path and research priorities aimed at closing these gaps.

Explainable Artificial Intelligence in Healthcare: Progress, Challenges, and Future Directions

This study aims to illustrate the significance of Abstract Science Intelligence (XAI) in the medical field, outlining its role in enhancing the interpretability of traditional "black-box" AI models.The goal of this research is to shed light on the significance of Abstract Science Intelligence (XAI) in the medical sector, elucidating its function in bolstering the interpretability of conventional "black-box" Artificial Intelligence models. In medical imaging, disease diagnosis, clinical decision support, and precision medicine, deep learning techniques have proven to be extremely successful; however, the inability to explain and understand how deep learning models work raises a variety of challenges for clinician trust, patient safety, ethical accountability, and regulatory compliance. This review provides a comprehensive overview of recent developments in XAI for healthcare, focusing on key methods for achieving interpretability, such as intrinsically interpretable models, and post-hoc explanation methods such as SHAP, LIME, Grad-CAM, attention mechanisms, surrogate models, and counterfactual explanations. A detailed review of the use of these methods in a variety of clinical areas such as radiology, oncology, cardiology, genomics, electronic health records and drug discovery is also given. Furthermore, the paper examines technical issues concerning explanation fidelity, computational complexity, robustness, scalability, and model validation, as well as ethical issues such as fairness, transparency, privacy, bias, and governance. Other research trends are also discussed, such as causal explainability, humancentered XAI, models that are uncertain, human-in-the-loop systems, and evaluation frameworks. In summary, the review highlights that explainability is not just a technical aspect but a key component in creating AI systems that are both trustworthy and clinically sound and can assist in safe and effective health care decision-making.