Data over dialogue: Why artificial intelligence is unlikely to humanise medicine

2025-04-10

AI Summary

This paper critically examines how artificial intelligence—particularly machine learning systems—affects the humanization of the physician–patient relationship, challenging the prevailing assumption that “AI makes medicine more humane.” Employing an integrative methodology combining philosophical analysis, medical ethics, human–computer interaction theory, and clinical reflection, the study systematically identifies, for the first time, the alienating mechanism by which healthcare AI replaces interpersonal dialogue with data-driven interaction in clinical settings. Findings demonstrate that ML systems implicitly erode five core dimensions of the physician–patient relationship—trust, care, empathy, mutual understanding, and communicative quality—rather than enhancing them. The work thus contests techno-optimist narratives and issues a critical ethical warning for AI governance in healthcare. It provides both a theoretical foundation and policy-relevant insights for reorienting intelligent medical practice toward human-centered values. (149 words)

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.

Problem

• AI may harm clinician-patient relationships
• Medical ML reduces trust and empathy
• AI negatively impacts care and communication

Innovation

• AI unlikely to humanize medicine
• Medical ML harms clinician-patient relationships
• Trust and empathy compromised by AI