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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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What Would it Take to get Biomedical QA Systems into Practice?

Gregory Kell¹, Iain J Marshall¹, Byron C Wallace²

¹King's College London.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing

|June 6, 2022

Summary

Medical question answering (QA) systems can help clinicians, but lack of trust hinders adoption. Focusing on transparency, trustworthiness, and provenance is key to developing reliable biomedical QA systems for practical use.

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Area of Science:

Biomedical Informatics
Natural Language Processing (NLP)
Clinical Decision Support

Background:

Medical question answering (QA) systems offer on-demand clinical insights from evidence.
Despite NLP advancements, medical QA systems see limited clinical adoption.
Lack of transparency, trustworthiness, and provenance hinders clinician trust in current QA outputs.

Purpose of the Study:

To identify criteria for enhancing the utility and trustworthiness of medical QA systems.
To address the gap between general QA progress and clinical application of medical QA.
To guide the development of more usable biomedical QA systems.

Main Methods:

Discussion of criteria for usable medical QA systems.
Assessment of existing medical QA models, tasks, and datasets against these criteria.
Identification of shortcomings in current approaches.

Main Results:

Current medical QA systems often lack transparency, trustworthiness, and provenance.
Existing datasets and tasks may not adequately support the development of trusted systems.
Specific criteria are proposed to improve medical QA system utility.

Conclusions:

Meeting proposed criteria for transparency, trustworthiness, and provenance is crucial for medical QA system adoption.
Future research should focus on developing and evaluating QA systems based on these criteria.
Enhanced medical QA systems have the potential to significantly aid clinical decision-making.