AI & Machine Learning
From Large Language Models (LLMs) automating documentation to predictive models flagging sepsis. We evaluate the clinical efficacy, safety guardrails, and liability frameworks of the AI revolution.
The "Black Box" Dilemma
Explainability vs. Accuracy: Deep learning models often achieve higher diagnostic accuracy than traditional regression models, but their decision-making process is opaque.
This creates a conflict with "Right to Explanation" laws (like GDPR) and physician liability. If an AI misses a diagnosis and the doctor cannot explain why, who is liable?
HTR Stance
We advocate for "Human-in-the-Loop" (HITL) verification systems until "Explainable AI" (XAI) matures enough to satisfy FDA auditing standards.
Generative AI (LLMs)
Use Case: Automated scribing and prior authorization appeals.
Risk: "Hallucinations" inserting false clinical data into EHRs.
Computer Vision
Use Case: Radiology read-assist and dermatology screening.
Risk: Training data bias leading to lower accuracy in diverse populations.
Algorithmic Bias & Equity
AI models trained on historical data inherit historical inequities. A famous algorithm used for population health management prioritized white patients over sicker black patients because it used "healthcare spending" as a proxy for "health need."
- ➜Data Representative: Training sets often under-sample minority groups.
- ➜Outcome Drift: Models degrade over time as patient demographics shift.
Mostly Radiology (77%)
Audit Your Algorithms
Our technical advisory team performs bias audits and validation studies for health AI deployments.
Schedule Tech Review