<p>Leveraging generative AI for interpretable clinical decision making through causal graphs</p>

Mehmet Eren Ahsen; Rand Kittani; Travis Gerke; Laya Krishnan; Sean Rogan; Erick R. Scott

doi:10.7490/f1000research.1120384.1

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Ahsen ME, Kittani R, Gerke T et al. Leveraging generative AI for interpretable clinical decision making through causal graphs [version 1; not peer reviewed]. F1000Research 2025, 14:1208 (poster) (https://doi.org/10.7490/f1000research.1120384.1)

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Leveraging generative AI for interpretable clinical decision making through causal graphs

Mehmet Eren Ahsen, Rand Kittani, Travis Gerke, Laya Krishnan, Sean Rogan, Erick R. Scott¹

Published 04 Nov 2025 (https://doi.org/10.7490/f1000research.1120384.1)

Author Affiliations

¹ cStructure LLC, La Jolla, CA, USA

Copyright © 2025 Ahsen ME et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 04 Nov 2025

Leveraging generative AI for interpretable clinical decision making through causal graphs
[version 1; not peer reviewed]

Mehmet Eren Ahsen, Rand Kittani, Travis Gerke, Laya Krishnan, Sean Rogan, Erick R. Scott¹

Author Affiliations

¹ cStructure LLC, La Jolla, CA, USA

Presented at

Pacific Symposium on Biocomputing (PSB) 2025

Abstract

Competing Interests

E.R. Scott and T. Gerke are members of cStructure LLC. S. Rogan is an employee of cStructure LLC. This work utilized the cStructure causal inference platform.

Keywords

Causal Inference, Large Language Models, Artificial Intelligence, Clinical Informatics, Structural Causal Models, AI-Supported Interactive Evaluation

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