Generative AI for Evidence-Based Medicine: A PICO GenAI for Synthesizing Clinical Case Reports
Bibliographic record
Abstract
Clinical research and practice are generating important new findings at exponential rate which need to be readily available to clinicians. However, clinicians are confronted with serious challenges when they try to seek such information for their evidence-based decision making or to generate new clinical case report. One important challenge is the long time needed to browse, filter, summarize and compile information from different resources. The other important challenge is to identify relevant important evidence-based information resources required to answer clinical questions or support a clinical finding. Artificial intelligence can help in solving both challenges based on the automatic question answering (Q&A) and generative technologies. However, Q&A and generative techniques are not trained to answer clinical queries that can be used for evidence-based practice nor it can respond to structured clinical questioning protocol like PICO (Patient/Problem, Intervention, Comparison and Outcome). This article describes the use of deep learning techniques for Q&A that is based on generative models like BERT and GPT to answer PICO clinical questions that can be used for evidence-based practice extracted from sound medical research resources like PubMed. We are reporting acceptable clinical answers that are supported by findings from PubMed. Our generative methods are reaching state of the art performance based on two staged bootstrapping process involving filtering relevant articles followed by identifying articles that support the requested outcome expressed by the PICO question.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".