STEM Strategies for Building Interpretable AI in Clinical Applications

Abstract

In the evolving landscape of healthcare, the integration of Artificial Intelligence (AI) into clinical decision-making holds immense promise for enhancing diagnostic accuracy, treatment planning, and patient outcomes. However, the adoption of AI in clinical applications is often hindered by concerns regarding transparency, accountability, and interpretability. This paper explores Science, Technology, Engineering, and Mathematics (STEM)-based strategies to develop interpretable AI models tailored for clinical environments. Emphasis is placed on integrating domain knowledge with machine learning algorithms, utilizing explainable AI (XAI) frameworks, and promoting interdisciplinary collaboration among clinicians, data scientists, and engineers. Techniques such as decision trees, attention mechanisms, and feature attribution models are examined for their potential to produce interpretable outputs without compromising predictive performance. Moreover, the role of human-centered design in model development is highlighted, ensuring that AI tools are intuitive and trustworthy for healthcare providers. Real-world case studies, including AI-assisted radiology and electronic health record analysis, are presented to demonstrate practical implementations and associated challenges. Ethical considerations, particularly those involving data privacy, bias mitigation, and patient consent, are also discussed as integral components of responsible AI deployment. This work underscores the necessity of embedding interpretability into the core design of clinical AI systems, advocating for regulatory frameworks and educational initiatives that empower practitioners to critically engage with AI tools. Ultimately, the successful integration of interpretable AI in clinical settings requires a robust STEM foundation, a commitment to ethical standards, and continuous dialogue between technology developers and medical professionals.