The Ethics of Clinical & Medical Decision Making

Improved Diagnostic Accuracy: AI can analyze vast amounts of data rapidly and detect subtle patterns beyond human perception, improving early diagnosis.
Consistency: AI systems provide consistent outputs, reducing human variability and fatigue-related errors.
Efficiency: Automates routine analysis (e.g., imaging, lab results), speeding up workflows and freeing clinicians for complex tasks.
Personalized Treatment: AI can integrate genetic, clinical, and lifestyle data to tailor treatments to individual patients.
Continuous Learning: AI models can improve over time as they process more data.
Decision Support: Provides evidence-based recommendations, alerts, and reminders that help clinicians make informed decisions.
Reducing Cognitive Load: Assists with complex data interpretation, reducing cognitive overload on clinicians.

False Positives and False Negatives: AI may incorrectly flag healthy patients (false positives), causing unnecessary stress and procedures, or miss diseases (false negatives), delaying treatment.
Training Data Bias & Quality Issues: AI trained on incomplete, biased, or non-representative data can produce misleading or harmful recommendations.
Lack of Explainability: Many AI models, especially deep learning, act as "black boxes," making it hard to understand or trust their decisions.
Over-reliance & Deskilling: Clinicians may become dependent on AI outputs, potentially eroding diagnostic skills or critical thinking.
Limited Context Understanding: AI may not fully grasp nuanced patient histories, socio-economic factors, or atypical presentations.
Integration Challenges: Difficulty embedding AI smoothly into clinical workflows and electronic health records.
Regulatory & Validation Gaps: Some AI tools may lack rigorous clinical validation or regulatory approval.

Patient Harm: Erroneous AI decisions can lead to misdiagnosis, inappropriate treatment, or delayed care.
Legal Liability: Unclear responsibility when AI contributes to clinical errors—liability may be ambiguous between clinicians, AI developers, and institutions.
Data Privacy & Security: Patient data used for AI training and inference must be protected against breaches.
Bias & Health Inequities: AI models reflecting biases in training data can worsen disparities among minority or underserved populations.
Alert Fatigue: Excessive AI-generated alerts may overwhelm clinicians, causing important warnings to be missed.
Malpractice & Trust Erosion: AI errors could undermine patient trust in clinicians and healthcare systems.
Resource Misallocation: False positives might lead to unnecessary tests, increasing costs and patient burden.

Transparency & Explainability: Patients and clinicians need understandable explanations for AI-driven decisions to provide informed consent.
Accountability: Clear frameworks are needed to assign responsibility for AI-assisted decisions.
Preserving Human Judgment: AI should support—not replace—clinician expertise; maintaining professional autonomy is critical.
Informed Consent: Patients should know if AI is used in their diagnosis or treatment planning.
Bias Mitigation: Actively identifying and correcting biases to ensure fairness and equity.
Data Consent and Usage: Ensuring patients consent to their data being used for AI training and that it is used responsibly.
Avoiding Overdependence: Safeguards to prevent clinicians from deferring blindly to AI without critical assessment.
Access and Equity: Ensuring AI benefits are available broadly, not just in resource-rich settings.

Francesca Tabor5 July 2025