The increasing integration of artificial intelligence into medicine, particularly in diagnostics and drug discovery, signals a shift within healthcare’s status quo. This literature review offers a critical, dualfocused analysis of this evolving landscape, addressing how deep neural networks (DNNs) and generative AI significantly improve diagnostic accuracy and accelerate drug discovery processes. Concurrently, it examines the main ethical and legal challenges inherent in implementing AI for medical diagnosis and drug discovery, including fundamental issues of data privacy, algorithmic transparency, accountability, and the adequacy of existing regulatory frameworks. By comprehensively analyzing both the potential benefits and the inherent challenges, this review attempts to provide a nuanced understanding of AI’s current and future role in medicine, offering insights essential for responsible innovation and successful integration into clinical practice and pharmaceutical research.