Generative AI for Evidence-Based Medicine: A PICO GenAI for Synthesizing Clinical Case Reports

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.

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