Journal of Medical Ethics · 2020 · 218 citations · 18 references
ComputingComputer ScienceHealth InformaticsArtificial IntelligenceClinical Decision SupportMachine LearningDeep LearningNeural NetworksPatient-provider RelationshipsPhysician-patient RelationshipShared Decision-makingDeep Learning DetectionHealthcare Trust BuildingMachine Learning For HealthcarePhysician-patient TrustTrust In Artificial IntelligenceTrust In Clinical RelationshipsTrust In Health CareTrustworthy Ai
Artificial intelligence promises to transform medicine, with deep‑learning models successfully detecting diabetic retinopathy, predicting readmissions, and aiding drug discovery, yet it raises concerns about eroding trust in clinical relationships. The paper argues that while AI systems can be reliable, they cannot be trusted or be trustworthy. The study finds that patient reliance on AI for medical decisions can erode trust in clinician–patient relationships.
Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.
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