Ethics Statement & Project Overview
Responsible AI development requires rigorous critical thinking about risks, failure modes, and societal impact. This page documents our ethical considerations for deploying LLMs in medical contexts.
Risk Analysis
Hallucination in Clinical Contexts
CRITICALLLMs can generate medically plausible but factually incorrect information with high confidence. In clinical settings, this can lead to misdiagnosis, incorrect treatment recommendations, or drug interactions being missed.
Bias and Fairness
HIGHMedical LLMs trained on biased datasets may underperform for underrepresented populations — including racial minorities, women, elderly patients, and those with rare diseases — perpetuating healthcare disparities.
Reliability and Consistency
HIGHLLMs are non-deterministic. The same question may receive different answers across sessions, making it difficult to establish clinical reliability standards or regulatory approval pathways.
Patient Autonomy and Consent
MEDIUMPatients may not realize they are receiving AI-generated medical information. Transparency about AI involvement in care decisions is an ethical imperative under principles of informed consent.
Ethical Principles
- 01.
All LLM outputs in clinical contexts must be reviewed and validated by qualified medical professionals.
- 02.
AI should augment — never replace — clinical judgment. It is a decision-support tool, not a decision-maker.
- 03.
Models must be evaluated on diverse demographic and linguistic datasets before deployment.
- 04.
Confidence scores and uncertainty estimates must be communicated to end users.
- 05.
Patients must be informed when AI plays a role in their care and have the right to opt out.
- 06.
Evaluation pipelines (like this one) must be reproducible, transparent, and open to peer review.
This platform is a research prototype intended for academic evaluation only. It is NOT a clinical decision support system. Results generated here should NOT be used to inform real medical decisions. Always consult qualified healthcare professionals for medical advice.
About This Project
Project Motivation
As LLMs become increasingly integrated into healthcare workflows — from clinical decision support to patient chatbots — rigorous evaluation of their reliability becomes an AI safety imperative. This project provides a systematic, reproducible framework for benchmarking LLM performance on medical QA tasks.
Research Goals
- → Quantify hallucination rates across different prompting strategies
- → Compare open and closed-source model reliability on medical benchmarks
- → Develop reproducible evaluation methodology for medical AI systems
- → Contribute to responsible AI deployment guidelines in healthcare