MACHINE LEARNING ALGORITHMS: CASE STUDIES, ETHICAL FRAMEWORKS AND FUTURE DIRECTIONS FOR RESPONSIBLE HEALTHCARE AI

Machine learning (ML) has become a central technology in contemporary healthcare, enabling models to learn fromdata and execute complex tasks. This article introduces the main types of ML algorithms, presents real-world casestudies that validate their effectiveness in healthcare, discusses consumer trust factors, and introduces emerginggovernance frameworks for ethical and responsible AI. Among the case studies are the employment of deep learningfor breast cancer detection, supervised models for diabetes complication prediction, and ensemble methods for heartdisease classification. The survey results reveal that AI is already widely used in health systems, yet issues of privacyand transparency are still raised. To close the trust gap, initiatives like FUTURE-AI and FAIR-AI offer fairness,universality, traceability, usability, robustness and explainability as their core values. Ethical frameworks revolvearound principles of autonomy, beneficence, non-maleficence, justice and transparency, whereas the industry guidancemainly focuses on oversight, data governance and accountability. A comparison table outlines the main frameworksand points to the future of responsible AI in healthcare, such as dynamic risk assessment, harmonized regulation andstakeholder engagement.