Explainable Artificial Intelligence in Medicine: Social and Ethical Issues

Abstract

Society faces a range of legal and ethical dilemmas arising from the advent of artificial intelligence (AI). These encompass concerns such as safeguarding privacy and preventing unwarranted surveillance, addressing bias and potential discrimination inherent in AI systems, and grappling with the profound philosophical question of how AI impacts human judgment. In fields like healthcare, errors in protocols can lead to catastrophic consequences for patients. The foundation of clinical medicine lies in transparent, evidence-based practices that guide physicians’ decisions. Presently, AI algorithms are being harnessed to supplement or replace certain functions of medical professionals, yet their integration has not gained widespread traction. A primary factor cited by medical experts is the inherent lack of transparency in specific AI algorithms, particularly opaque black-box models that contravene medical ethics. The opacity of these algorithms raises apprehensions about the rationale behind their decisions, often influenced by erroneous or unaccounted variables. The absence of a comprehensible decision-making process erodes patient's trust. Despite this, considering the limitations of some AI algorithms, there is ample room for advocating AI alongside validation approaches like randomized controlled trials. This study proposes the integration of explainable artificial intelligence (XAI) to address the transparency predicament associated with certain AI applications. Employing an XAI framework can align model performance with clinical objectives, making AI adoption less precarious for medical practitioners. This chapter offers a thorough exploration of XAI methodologies and outlines how a dependable AI can emerge by elucidating AI models in healthcare contexts. This contribution seeks to advance the formalization of the XAI field.

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