Establishing Responsibility Systems for AI Application in Clinical Decision-Making
A Relational Responsibility Perspective
Bibliographic Data
| ID | 24217820 |
|---|---|
| Authors | Xiyi Chen (0009-0006-8415-4059, Shanghai Jiao Tong University) |
| Year | 2026 |
| Pages | 1-24 |
| Publication date | 2026-09-28 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Asian Journal of Law and Society (JOURNAL) |
| Journal identifiers | ISSN: 2052-9015 • E-ISSN: 2052-9023 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/als.2026.10070 |
| OpenAlex | W7214672374 |
| Language | EN |
| References cited | 11 |
AI in clinical decision-making challenges liability frameworks grounded in subject–object dichotomies, linear causation, and atomised attribution. Drawing on relational responsibility, this article argues that the human–machine association, rather than the AI system or clinician alone, should be treated as the fundamental decision-making unit. Responsibility should therefore emphasise interaction quality, harm prevention, and relationships that sustain future cooperation. The article proposes dialogic responsibilities for technology providers, medical institutions, clinicians, and patients. For external compensation, medical institutions should act as the primary responsibility hub, reducing patients’ burden of proof while retaining rights of internal recourse based on specific human–machine interaction patterns. Internally, liability should reflect calibrated trust, documented dialogue, and the respective commitments of technology providers and clinical users. It thereby promotes accountable collaboration throughout the clinical AI lifecycle. By linking physician–patient trust with algorithmic trust, this relational framework seeks to reconcile technological innovation, professional autonomy, and the protection of patients’ rights.
algorithmic trust · Burden of proof · clinical AI · clinical decision-making · harm prevention · human-machine interaction · Liability · medical institutions · Patient Rights · professional autonomy · relational responsibility · technology providers · Artificial Intelligence in Healthcare and Education · Digitalization, Law, and Regulation · Medical Malpractice and Liability Issues
Understanding the dilemma of explainable artificial intelligence
How the different explanation classes impact trust calibration
Bridging the accountability gap of artificial intelligence – what can be learned from Roman law
Human–algorithm hybrids as (quasi‐)organizations? On the accountability of digital collective actors
| Highly cited | No |
|---|