Investigating Patient Perspectives on the Use of AI in Medical Diagnosis and Drug Prescription: A Work-in-Progress

Patients’ trust will make or break AI-enabled care. This work-in-progress shares early findings from a pilot survey (n=5) on how patients perceive AI in diagnosis and prescribing- what they understand, where they’re comfortable, what worries them, and how much human oversight they expect. Respondents showed moderate understanding and comfort with AI, but elevated concerns about privacy/accuracy and a strong preference for physician involvement (human-in-the-loop). We translate these signals into practical design implications for healthcare systems-transparency, robust data protection, and workflow safeguards-then outline our scale-up plan: recruit a larger, more diverse sample; incorporate validated attitudinal items and richer open-ended prompts; and apply inferential analyses to identify predictors of acceptance. Attendees will leave with patient-centered checkpoints to guide safe, trusted AI implementation in clinical pathways. 

Explain patient-derived requirements for AI-enabled diagnosis/prescribing

Design human-in-the-loop workflows incorporating explainability, validation, and data-privacy controls