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Chapter

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Title

Manually-Curated Versus LLM-Generated Explanations for Complex Patient Cases: An Exploratory Study with Physicians

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2024

Chapter type

chapter in monograph / paper

Publication language

english

Abstract

EN Multimorbdity guideline-based clinical decision support systems (MGCDSSes) have emerged to optimize outcomes for multimorbid patients by generating personalized treatment plans that consider many clinical data sources. The success of these systems relies on their ability to explain treatment rationale, fostering trust in their outcomes among physicians. While traditionally developing treatment explanations required significant manual effort from physicians, the emergence of large language models (LLMs) offers potential to automate and simplify this process. LLMs like Meditron70B have shown promise in generating treatment explanations, saving time and resources for physicians. However, questions remain regarding the accuracy and depth of LLM-generated explanations. In this work, we evaluate the performance of Meditron70B in generating treatment explanations within our MitPlan MGCDSS using a physician-focused survey. We highlight both the promise and potential limitations of using LLMs for this purpose.

Date of online publication

25.07.2024

Pages (from - to)

313 - 323

DOI

10.1007/978-3-031-66535-6_33

URL

https://link.springer.com/chapter/10.1007/978-3-031-66535-6_33

Book

Artificial Intelligence in Medicine : 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II

Presented on

22nd International Conference on Artificial Intelligence in Medicine AIME 2024, 9-12.07.2024, Salt Lake City, USA

Ministry points / chapter

20

Ministry points / conference (CORE)

70

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