Enterprise AI Analysis
Trust and Artificial Intelligence in the Doctor-Patient Relationship: Epistemological Preconditions and Reliability Gaps
This paper investigates the complex interplay of trust, artificial intelligence (AI), and the doctor-patient relationship, highlighting that traditional notions of trust, grounded in human qualities like empathy and accountability, cannot be directly applied to AI systems. Instead, the use of AI in healthcare introduces 'reliability gaps' due to its opaque nature, which prevents both doctors and patients from independently verifying its performance. The study proposes a framework for evaluating AI reliability based on computational reliabilism and examines three use cases: melanoma detection, Alzheimer's risk prediction, and psychotherapy chatbots. It concludes that as AI systems become more autonomous and opaque, trust in the doctor becomes increasingly crucial for bridging these reliability gaps, potentially overburdening the doctor's role and shifting patient reliance from cognitive evaluation to affective trust.
Key Findings at a Glance
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Core AI Reliability Criteria
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Impactful Pages Analyzed
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Key Concepts Extracted
Deep Analysis & Enterprise Applications
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Trust vs. Reliance
AI 'Trustworthiness'
The Reliability Gap
Epistemic Foundations of Trust
Trust involves a three-part relation (A trusts B to do X) and requires the trustee's willingness and competence. It goes beyond mere reliance, which is about expecting something to act in a specific manner without requiring affective or normative stance. AI systems can only be relied upon, not truly trusted, due to their lack of moral agency, reciprocity, and accountability.
Epistemic Foundations of Trust
The concept of 'trustworthy AI' as formulated by HLEGOAI primarily refers to the socio-technical context and external assurances (e.g., laws, technical robustness, ethical principles) rather than intrinsic qualities of the AI itself. If AI is fully explicable, trust is not needed; it becomes necessary in situations of heightened epistemic vulnerability due to opacity.
Epistemic Foundations of Trust
A central finding is the 'reliability gap'—a conceptual space where the opaque nature of advanced AI systems prevents both doctors and patients from independently verifying their reliability. This gap necessitates increased reliance on the doctor as a proxy for AI's performance, potentially overburdening their fiduciary role.
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Reliability Criteria for AI Systems
The paper leverages Computational Reliabilism, outlining three core criteria for assessing AI reliability to address 'reliability gaps': (1) Verification and Validation, (2) Robustness, and (3) Implementation History. These criteria are crucial for evaluating if an AI system can be dependably assessed.
AI Integration Process in Doctor-Patient Relationship
Understanding how AI impacts trust requires evaluating epistemic attitudes across various stages of interaction, from initial introduction to bridging reliability gaps.
AI Introduction in Healthcare
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Epistemic Asymmetry Evaluation
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Assessment of AI Reliability (3 Criteria)
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Impact on Doctor-Patient Trust
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Bridging Reliability Gaps
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Case Study: Psychotherapy Chatbots
Scenario: AI-driven chatbots are used in blended treatment or for preliminary symptom assessments. They operate with high autonomy, low determinism, and adaptive mechanisms, making transparency and full explainability challenging. Reliability is hard to verify due to individualized psychotherapy outcomes. Doctors must trust the broader socio-technical system, and patients may even prefer chatbots to human therapists.
Impact: This creates a significant reliability gap. Doctors cannot independently verify chatbot reliability, forcing reliance on external system guarantees. This burdens the doctor's role and complicates patient trust, as chatbot failures could erode confidence in the doctor's competence, while chatbot success might undermine the doctor's central role.
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Your AI Implementation Roadmap
A strategic, phased approach to successfully integrate AI, mitigate risks, and maximize value.
Phase 1: Epistemic Audit
Conduct a comprehensive audit of existing data sources and AI models to identify potential reliability gaps and areas of opacity. Define clear criteria for verification and validation specific to your medical AI applications.
Phase 2: Stakeholder Alignment
Engage doctors, patients, and AI developers to establish shared understanding of AI capabilities and limitations. Develop clear communication strategies for explaining AI outputs and fostering patient confidence.
Phase 3: Robustness & Explainability Framework
Implement robust testing protocols for AI systems, including adversarial testing and bias detection. Develop explainable AI (XAI) tools to provide physicians with actionable insights into AI's reasoning, even for 'black box' models.
Phase 4: Continuous Monitoring & Feedback
Establish continuous monitoring of AI performance in real-world clinical settings. Create feedback loops for doctors and patients to report issues and contribute to ongoing AI model refinement and retraining.
Phase 5: Ethical & Governance Integration
Integrate ethical principles (autonomy, non-harm, fairness, explicability) into AI development and deployment. Formalize accountability frameworks and institutional oversight to bridge responsibility gaps and reinforce trust in the socio-technical system.
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