Explainable AI for Interpretability and Trust in Medical Diagnosis
Implements explainable AI techniques to provide transparent explanations for medical diagnoses, enhancing trust and acceptance among healthcare professionals and patients
Keywords:
Explainable AI, Interpretability, Trust, Medical Diagnosis, Healthcare, Transparency, Machine Learning, XAI Techniques, Healthcare Professionals, PatientsAbstract
Explainable Artificial Intelligence (XAI) has emerged as a critical area of research, particularly in the medical domain, where the decisions made by AI systems can have a profound impact on human lives. This paper explores the application of XAI techniques to enhance the interpretability and trustworthiness of AI-driven medical diagnosis. By providing transparent explanations for the reasoning behind AI-generated diagnoses, XAI can bridge the gap between complex AI models and end-users, including healthcare professionals and patients. The implementation of XAI in medical diagnosis not only improves the understanding of AI-generated decisions but also enhances trust and acceptance of AI systems in healthcare settings. This paper presents a comprehensive overview of XAI techniques, their applications in medical diagnosis, and the implications for healthcare stakeholders. Through case studies and discussions, the paper highlights the benefits and challenges of implementing XAI in medical diagnosis and provides recommendations for future research directions.
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