IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM

IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM

Multi-disease conditions strain the body’s defenses, complicating recovery and increasing mortality risk. Therefore, effective concurrent prevention of multiple diseases is essential for mitigating complications and improving overall well-being. Explainable artificial intelligence (XAI) with an adva...

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Bibliographic Details
Main Authors: Prasant Kumar Mohanty, Sharmila Anand John Francis, Rabindra Kumar Barik, K. Hemant Kumar Reddy, Diptendu Sinha Roy, Manob Jyoti Saikia
Format: Article
Language:English
Published: PeerJ Inc. 2025-04-01
Series:PeerJ Computer Science
Subjects:
LLM
Explainable AI
Large language model
NSGA-II
Healthcare
Treatment
Online Access:https://peerj.com/articles/cs-2839.pdf
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Summary:Multi-disease conditions strain the body’s defenses, complicating recovery and increasing mortality risk. Therefore, effective concurrent prevention of multiple diseases is essential for mitigating complications and improving overall well-being. Explainable artificial intelligence (XAI) with an advanced multimodal large language model (LLM) can create an interactive system enabling the general public to engage in natural language without any specialized knowledge prerequisites. Counterfactual explanation, an XAI method, offers valuable insights by suggesting adjustments to patient features to minimize disease risks. However, addressing multiple diseases simultaneously poses challenging barriers. This article proposes an interactive multi-disease prevention system that uses Google Gemini Pro, a multimodal LLM, and a non-dominated sorting genetic algorithm, namely NSGA-II, to overcome such problems. This system recommends changes in feature values to concurrently minimize the risk of diseases such as heart attacks and diabetes. The system facilitates personalized feature value selection, significantly reducing disease attack probabilities to as low as possible. Such an approach holds the potential to simultaneously address the unresolved issue of preventing and managing multiple diseases for the general public.
ISSN:2376-5992

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