Disclaimer: This project is a research prototype. It does not provide medical advice, diagnosis, or treatment. It is not a medical device and must be used under clinician supervision or for educational purposes only.

1) Problem Statement

Early, low-stakes triage and health education are often inaccessible. A GenAI symptom checker aims to:

• Translate layperson descriptions into structured clinical signals (symptoms, onset, severity).
• Offer risk-aware guidance (self-care vs. seek care now vs. emergency).
• Generate explainable outputs with uncertainty disclosure and safety rails.

2) Conceptual Scope (What it should/shouldn’t do)

Should:

• Collect symptoms with clarifying questions.
• Map text to standard clinical vocabularies (e.g., SNOMED CT/ICD terms).
• Estimate likelihood ranges for common, non-emergent conditions.
• Provide next-step guidance and red-flag detection with clear rationales.
• Encourage professional evaluation; surface urgent care triggers.

Shouldn’t:

• Provide definitive diagnoses or prescriptions.
• Replace clinician assessment, emergency services, or local guidelines.
• Handle high-risk differentials without strong fail-safes (route to “seek care now”).

3) Theoretical Architecture (High-Level)

1. Input Layer
   • Free-text complaint + optional metadata (age band, sex at birth, pregnancy status, vitals if available).
   • Safety filter on input (self-harm, overdose, child health, pregnancy complications → urgent escalation).
2. Clinical NLP & Structuring
   • Symptom extraction (NER) → “fever (38.5°C), 3 days, intermittent; dry cough; sore throat.”
   • Temporal parsing (onset, duration), severity scales, aggravators/relievers.
   • Ontology mapping (SNOMED CT / ICD-10) with confidence.
3. Reasoning & Triage Engine
   • Hybrid approach:
     • Knowledge-guided graph: symptom–condition relationships, red-flag rules.
     • Probabilistic layer: Bayesian/likelihood estimates over common conditions (with wide priors and uncertainty).
     • LLM chain-of-thought (hidden) + tool use for hypothesis generation and consistency checks.
   • Guardrails:
     • Deterministic red-flag rules override (e.g., chest pain + dyspnea → emergency).
     • Uncertainty calibration and refusal when confidence is low.
4. Explanation & Guidance
   • Plain-language summary of what was considered and why.
   • Care-setting recommendation (self-care, urgent care, ER) with safety net advice and return precautions.
   • Localization hooks for region-specific helplines (not bundled here).
5. Safety, Privacy, and Governance
   • PHI minimization; encryption at rest/in transit (theory design).
   • Audit logs for prompts, outputs, and rule-based overrides.
   • Bias, safety, and performance monitoring.

4) Knowledge Sources (Conceptual)

• Public health guidelines (triage criteria, red flags).
• Clinical ontologies (SNOMED CT, ICD-10) for consistent labeling.
• Peer-reviewed evidence summaries for common primary-care presentations.
• No direct training on private patient data unless governed by strict consent and compliance frameworks.

5) Reasoning Patterns

• Differential generation: from structured symptoms to candidate conditions.
• Elimination by red flags: immediate escalation when matched.
• Evidence aggregation: tally supportive/contradictory findings; compute qualitative likelihoods (low/medium/high).
• Counterfactual prompts: ask targeted follow-ups to reduce ambiguity.
• Uncertainty disclosure: present ranges, not absolutes.

6) Evaluation Strategy (Non-clinical, Research-only)

• Synthetic vignettes covering common complaints + rare but critical red flags.
• Checklist scoring:
  • Correct identification of any red flag (recall-weighted).
  • Appropriateness of triage level (confusion matrix).
  • Consistency and absence of hallucinated tests/treatments.
  • Readability and empathy metrics (Flesch/Kincaid + rubric).
• Human review: clinicians rate outputs (safety first).
• Drift monitoring: periodic re-checks as models/knowledge evolve.

Note: These are surrogate metrics; not a substitute for clinical validation or regulatory approval.

7) Error Taxonomy & Mitigations

• Missed red flag → hard-coded rule overrides; multi-pass safety checks.
• Over-reassurance → conservative priors + “when to seek care” blocks.
• Hallucination → retrieval-augmented grounding; citation requirements; refusals when unsure.
• Bias (age, sex, race proxies) → stratified evaluation, fairness audits, balanced exemplars.
• Over-triage → acceptable trade-off but monitored to avoid burdening services.

8) Ethical Use & Compliance (Conceptual)

• Non-maleficence: prefer false positives over false negatives for high-risk symptoms.
• Autonomy: transparent limitations; encourage clinician consultation.
• Justice: evaluate across demographics; accessible language.
• Privacy: minimal data retention; user control over deletion/export.
• Regulatory posture: research tool; if productized, pursue appropriate regulatory pathways (e.g., QMS, clinical studies).

9) Privacy & Security (Design Goals)

• Data minimization; optional local inference/de-identification.
• Encryption in transit/at rest; role-based access to logs.
• No third-party sharing without consent; clear data retention windows.
• Adversarial testing against prompt-injection and data leakage.

10) Human-in-the-Loop

• Route ambiguous or high-risk cases to a clinician review queue.
• Provide editable rationale and structured data for faster clinician triage.
• Learn safely from clinician feedback (controlled updates, no blind self-training).

11) Limitations

• Not calibrated for rare diseases or complex comorbidities.
• Cultural/linguistic nuances can affect symptom descriptions.
• Real-world performance depends on deployment context and population.

12) Future Work

• Calibrated uncertainty (conformal prediction for triage).
• Personalization with explicit consent (PMH, meds, allergies).
• Multimodal inputs (vitals/wearables) with strict privacy controls.
• Localization to guidelines and care pathways by region.
• Prospective clinical study with IRB oversight.

13) Repository Structure (Theory-Level)

/docs/           # Model cards, evaluation rubrics, risk registry
/data/           # Schema & ontology mappings (no PHI; placeholders only)
/policies/       # Safety, privacy, and governance docs
/notebooks/      # Research analyses (theoretical examples)
/src/            # Modular components: nlp/, triage/, safety/, explain/
/eval/           # Synthetic vignettes, metrics, reports

14) Citation (Example Template)

If you use this work, please cite:

GenAI-Medical-Symptom-Checker: A Research Framework for Safe, Explainable, and Privacy-Preserving AI Triage. (Year). GitHub repository. URL

Contact: Bhavesh Kallururate real-world implementation of Generative AI for personal assistants, showing how context + intelligence = true automation.