Table 1. Summary of Ethical Challenges and Applications of AI in Medicine.

SHAP: SHapley Additive eXplanations; LIME: Local Interpretable Model-agnostic Explanations; FIRM: Fairness in Machine Learning. 

Aspect	Details	Examples
Applications in Medicine	Diagnostics, personalized treatments, drug discovery, radiology, pathology, and GI disorder detection.	AI in colonoscopy for polyp detection.
AI in Drug Discovery	Protein structure prediction, RNA/DNA folding analysis, small-molecule virtual screening.	AlphaFold, AtomNet, Schrödinger platforms.
Ethical Challenges	Data privacy, bias, transparency, accountability, and workforce displacement.	Genomic data breaches, biased datasets.
Key Risks	Data breaches, re-identification of anonymized data, biased outcomes, opaque decision-making.	23andMe breach, algorithm bias in oncology.
Mitigation Strategies	Privacy-by-design, federated learning, diverse datasets, explainable AI, dynamic ethical oversight.	WHO 2023 Ethical Guidelines.
Validation	Experimental techniques to confirm AI predictions.	X-ray crystallography, NMR spectroscopy.
Workforce Implications	Job displacement concerns; opportunities in algorithm development and clinical trial optimization.	AI in radiology; reskilling initiatives.
Transparency Techniques	Enhancing interpretability of AI models with SHAP, LIME, and attention mechanisms.	Visual explanations for medical imaging.
Accountability Frameworks	Shared responsibility among developers, providers, and institutions.	Fairness in Machine Learning (FIRM).