• Imaging-driven AI research emphasizes end-to-end diagnostic workflows, standardized evaluation, and clinically meaningful endpoints in radiology, illustrated by CT-based COVID screening, imaging research roadmaps, and endpoint-focused AI in medical imaging [1], [10], [16].

• Ethical, regulatory, and transparency concerns shape AI adoption in medicine, highlighting explainability, accountability, safety, and governance across black-box debates and policy-oriented analyses [17], [18], [11], [9], [5], [13].

• Educational transformations accompany AI integration, advocating curricula reform, competency-based training, and clinician–AI interface design to prepare healthcare professionals for AI-enabled decision support [4], [6], [20], [8].

• Clinical decision support and treatment optimization research emphasize AI-driven strategies and risk stratification in critical care, including sepsis management, coronavirus severity framing, and AI‑ECG diagnostics [7], [15], [12].

• Foundational science and meta-research examine AI in health as an evolving field, addressing hype, adoption patterns, roadmaps, and bibliometric trends to guide robust deployment [2], [10], [3].