Physician Response Evaluation With Contextual Insights vs. Standard Engines - Artificial Intelligence RAG vs LLM Clinical Decision Support (PRECISE)

Montefiore Medicine Academic Health System

Status: Enrolling

Conditions: Large Language Models

Treatments: Other: GPT-4; Other: OpenEvidence

Study type: Interventional

Funder types: Other

Identifiers

NCT07037940

2025-16599

Details and patient eligibility

About

Clinical decision support tools powered by artificial intelligence are being rapidly integrated into medical practice. Two leading systems currently available to clinicians are OpenEvidence, which uses retrieval-augmented generation to access medical literature, and GPT-4, a large language model. While both tools show promise, their relative effectiveness in supporting clinical decision-making has not been directly compared. This study aims to evaluate how these tools influence diagnostic reasoning and management decisions among internal medicine physicians.

Full description

Internal medicine attendings and residents are invited to participate in a study investigating how physicians using a RAG-based LLM (OpenEvidence) perform compared to those using a standard general-purpose LLM (ChatGPT) on both diagnostic reasoning and complex management decisions. As AI tools increasingly enter clinical practice, evidence is needed about which approaches best support physician decision-making. This study will help determine if specialized medical knowledge retrieval systems (OpenEvidence) provide advantages over general AI assistants (ChatGPT) when solving real clinical cases.

Participants will complete one 90-minute Zoom session where clinical cases derived from real, de-identified patient encounters will be solved. Participants will be randomly assigned to use either OpenEvidence or ChatGPT and all responses evaluated by blinded scorers using a validated rubric.

Enrollment: 56 estimated patients

Sex: All

Ages: 25+ years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Internal medicine residents
• Internal medicine attending physicians

Exclusion criteria

• Not meeting Inclusion Criteria

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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56 participants in 2 patient groups

OpenEvidence

Active Comparator group

Description:

Participants in this arm will use OpenEvidence as their research tool

Treatment:

Other: OpenEvidence

ChatGPT

Active Comparator group

Description:

Patients in this arm will use Chat-GPT as their research tool

Treatment:

Other: GPT-4