Ethics, FDA & Patient–Provider Interaction (PPI) with AI

1) Ethical Foundations

• Beneficence & Non‑maleficence: AI must show net clinical benefit and avoid foreseeable harm.
• Autonomy: Patients should understand when and how AI influences their care and be free to opt out where feasible.
• Justice: Evaluate for disparate performance across demographics; mitigate inequality in access and outcomes.
• Accountability: Keep a clear line of human responsibility for decisions, overrides, and escalation.

2) FDA Concepts to Track (U.S.)

This is a high‑level orientation for planning; it is not legal or regulatory advice.

• SaMD framing: When software performs a medical function, it may fall under Software as a Medical Device considerations.
• Predetermined Change Control Plans (PCCP): Outline what you intend to update post‑market (e.g., model retraining) and how you’ll manage risk.
• Good ML Practices: Emphasize data quality, reproducibility, transparency, and robust evaluation.
• Post‑market surveillance: Monitor performance, usability, failures, and complaints; be ready to act.

3) Patient–Provider Interaction (PPI) Patterns

4) Operational Guardrails

• Document permitted and prohibited use‑cases; keep a living registry of models and versions.
• Bias & performance checks pre‑deployment and on a recurring schedule; publish a model factsheet.
• Real‑time monitoring of drift, alert fatigue, override rates, and adverse events.
• Security & privacy controls aligned to institutional policy and law; least‑privilege access.
• Incident response playbook for model outages, misbehavior, and data issues.

5) Documentation Set (Minimum)

• Clinical intent statement and decision pathway diagrams.
• Data lineage & quality report; training/validation cohorts.
• Validation metrics (sensitivity/specificity/PPV/NPV), subgroup analysis, and known limitations.
• User‑facing disclosures, consent language, and risk statements.
• Monitoring KPIs and governance roles with escalation contacts.

Quick Start: 12‑Point Readiness Scan

Resumen en Español (breve)

Guía práctica para IA clínica centrada en el paciente: fundamentos éticos (beneficencia, autonomía, justicia), ideas clave para FDA (SaMD, PCCP, buenas prácticas de ML, vigilancia pos‑mercado), patrones de interacción paciente‑profesional (transparencia, consentimiento, explicabilidad, escalado), guardas operativas y un set mínimo de documentación para gobernanza y cumplimiento.