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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-04-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2839.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850196384276283392 |
|---|---|
| author | Prasant Kumar Mohanty Sharmila Anand John Francis Rabindra Kumar Barik K. Hemant Kumar Reddy Diptendu Sinha Roy Manob Jyoti Saikia |
| author_facet | Prasant Kumar Mohanty Sharmila Anand John Francis Rabindra Kumar Barik K. Hemant Kumar Reddy Diptendu Sinha Roy Manob Jyoti Saikia |
| author_sort | Prasant Kumar Mohanty |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d807103e6a46420489b23e211bbe4fb5 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-d807103e6a46420489b23e211bbe4fb52025-08-20T02:13:28ZengPeerJ Inc.PeerJ Computer Science2376-59922025-04-0111e283910.7717/peerj-cs.2839IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLMPrasant Kumar Mohanty0Sharmila Anand John Francis1Rabindra Kumar Barik2K. Hemant Kumar Reddy3Diptendu Sinha Roy4Manob Jyoti Saikia5Department of Computer Science and Engineering, National Institute of Technology, Shillong, Meghalaya, IndiaDepartment of Computer Science, Rijal Alma’a, King Khalid University, Abha, Saudi ArabiaSchool of Computer Applications, KIIT Deemed to be University, Bhubaneswar, Odisha, IndiaDepartment of Computer Science and Engineering, VIT-AP University, Beside AP Secretariat Amaravati, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, National Institute of Technology, Shillong, Meghalaya, IndiaBiomedical Sensors & Systems Lab, The University of Memphis, Memphis, TN, United StatesMulti-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.https://peerj.com/articles/cs-2839.pdfLLMExplainable AILarge language modelNSGA-IIHealthcareTreatment |
| spellingShingle | Prasant Kumar Mohanty Sharmila Anand John Francis Rabindra Kumar Barik K. Hemant Kumar Reddy Diptendu Sinha Roy Manob Jyoti Saikia IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM PeerJ Computer Science LLM Explainable AI Large language model NSGA-II Healthcare Treatment |
| title | IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM |
| title_full | IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM |
| title_fullStr | IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM |
| title_full_unstemmed | IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM |
| title_short | IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM |
| title_sort | impact an interactive multi disease prevention and counterfactual treatment system using explainable ai and a multimodal llm |
| topic | LLM Explainable AI Large language model NSGA-II Healthcare Treatment |
| url | https://peerj.com/articles/cs-2839.pdf |
| work_keys_str_mv | AT prasantkumarmohanty impactaninteractivemultidiseasepreventionandcounterfactualtreatmentsystemusingexplainableaiandamultimodalllm AT sharmilaanandjohnfrancis impactaninteractivemultidiseasepreventionandcounterfactualtreatmentsystemusingexplainableaiandamultimodalllm AT rabindrakumarbarik impactaninteractivemultidiseasepreventionandcounterfactualtreatmentsystemusingexplainableaiandamultimodalllm AT khemantkumarreddy impactaninteractivemultidiseasepreventionandcounterfactualtreatmentsystemusingexplainableaiandamultimodalllm AT diptendusinharoy impactaninteractivemultidiseasepreventionandcounterfactualtreatmentsystemusingexplainableaiandamultimodalllm AT manobjyotisaikia impactaninteractivemultidiseasepreventionandcounterfactualtreatmentsystemusingexplainableaiandamultimodalllm |