An Ethical Implications of Machine Learning in Medicine Using Optimized Ethical-Epistemic Matrices

Abstract

With widespread adoption of machine learning within medical domains, it's time to critically analyze their ethical impact because these are changing diagnostics, treatment planning, and patient care landscapes. Many reviews lack critical, domain-specific synthesis and tend to be narrow because they only focus on either technical progress or isolated issues in process. This paper fills these gaps by providing an iterative, systematic review of the ethical dimensions of ML in medicine, covering a wide range of medical specialties from psychiatry, neuro-oncology, cardiology to neonatal care sets. This paper adopts a multi-method approach that combines narrative reviews, systematic analyses, regulatory evaluations, and conceptual frameworks. The methodologies applied will be the PRISMA framework for systematic synthesis, matrices for structured ethical evaluation based on ethical-epistemic, and performance metric analysis for ML models, such as CNNs, random forests, and hybrid approaches. These methods ensure that the technical and the ethical dimensions involved would undergo comprehensive evaluation to derive insights related to accountability, transparency, and trustworthiness. This approach gives a granular understanding of strengths and weaknesses in specific models but addresses concerns that are critical, like bias, regulatory compliance, and fair access. The conclusions are that deep learning models are powerful for precision and scale but simpler algorithms do keep relevance for applications that require interpretability sets. This work underlines the need for hybrid models and regulatory frameworks in filling the gap between technological advancement and ethical stewardship. The strong, cross-disciplinary study here not only furthers the discussion concerning ethical AI in medicine but also offers some practical insights to practitioners and policymakers and to researchers of AI in the process.

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