Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review
BackgroundChronic diseases are a major global health burden, accounting for nearly three-quarters of the deaths worldwide. Large language models (LLMs) are advanced artificial intelligence systems with transformative potential to optimize chronic disease management; however,...
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JMIR Publications
2025-04-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e70535 |
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| author | Caixia Li Yina Zhao Yang Bai Baoquan Zhao Yetunde Oluwafunmilayo Tola Carmen WH Chan Meifen Zhang Xia Fu |
| author_facet | Caixia Li Yina Zhao Yang Bai Baoquan Zhao Yetunde Oluwafunmilayo Tola Carmen WH Chan Meifen Zhang Xia Fu |
| author_sort | Caixia Li |
| collection | DOAJ |
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BackgroundChronic diseases are a major global health burden, accounting for nearly three-quarters of the deaths worldwide. Large language models (LLMs) are advanced artificial intelligence systems with transformative potential to optimize chronic disease management; however, robust evidence is lacking.
ObjectiveThis review aims to synthesize evidence on the feasibility, opportunities, and challenges of LLMs across the disease management spectrum, from prevention to screening, diagnosis, treatment, and long-term care.
MethodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines, 11 databases (Cochrane Central Register of Controlled Trials, CINAHL, Embase, IEEE Xplore, MEDLINE via Ovid, ProQuest Health & Medicine Collection, ScienceDirect, Scopus, Web of Science Core Collection, China National Knowledge Internet, and SinoMed) were searched on April 17, 2024. Intervention and simulation studies that examined LLMs in the management of chronic diseases were included. The methodological quality of the included studies was evaluated using a rating rubric designed for simulation-based research and the risk of bias in nonrandomized studies of interventions tool for quasi-experimental studies. Narrative analysis with descriptive figures was used to synthesize the study findings. Random-effects meta-analyses were conducted to assess the pooled effect estimates of the feasibility of LLMs in chronic disease management.
ResultsA total of 20 studies examined general-purpose (n=17) and retrieval-augmented generation-enhanced LLMs (n=3) for the management of chronic diseases, including cancer, cardiovascular diseases, and metabolic disorders. LLMs demonstrated feasibility across the chronic disease management spectrum by generating relevant, comprehensible, and accurate health recommendations (pooled accurate rate 71%, 95% CI 0.59-0.83; I2=88.32%) with retrieval-augmented generation-enhanced LLMs having higher accuracy rates compared to general-purpose LLMs (odds ratio 2.89, 95% CI 1.83-4.58; I2=54.45%). LLMs facilitated equitable information access; increased patient awareness regarding ailments, preventive measures, and treatment options; and promoted self-management behaviors in lifestyle modification and symptom coping. Additionally, LLMs facilitate compassionate emotional support, social connections, and health care resources to improve the health outcomes of chronic diseases. However, LLMs face challenges in addressing privacy, language, and cultural issues; undertaking advanced tasks, including diagnosis, medication, and comorbidity management; and generating personalized regimens with real-time adjustments and multiple modalities.
ConclusionsLLMs have demonstrated the potential to transform chronic disease management at the individual, social, and health care levels; however, their direct application in clinical settings is still in its infancy. A multifaceted approach that incorporates robust data security, domain-specific model fine-tuning, multimodal data integration, and wearables is crucial for the evolution of LLMs into invaluable adjuncts for health care professionals to transform chronic disease management.
Trial RegistrationPROSPERO CRD42024545412; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024545412 |
| format | Article |
| id | doaj-art-b048f513b13c455cbe692c77d0c540df |
| institution | OA Journals |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | JMIR Publications |
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| series | Journal of Medical Internet Research |
| spelling | doaj-art-b048f513b13c455cbe692c77d0c540df2025-08-20T02:17:33ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-04-0127e7053510.2196/70535Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic ReviewCaixia Lihttps://orcid.org/0000-0002-8170-432XYina Zhaohttps://orcid.org/0009-0005-1747-4952Yang Baihttps://orcid.org/0000-0002-3789-0540Baoquan Zhaohttps://orcid.org/0000-0002-0574-1663Yetunde Oluwafunmilayo Tolahttps://orcid.org/0000-0001-9408-0694Carmen WH Chanhttps://orcid.org/0000-0003-0696-2369Meifen Zhanghttps://orcid.org/0000-0001-7931-3285Xia Fuhttps://orcid.org/0000-0001-6480-7717 BackgroundChronic diseases are a major global health burden, accounting for nearly three-quarters of the deaths worldwide. Large language models (LLMs) are advanced artificial intelligence systems with transformative potential to optimize chronic disease management; however, robust evidence is lacking. ObjectiveThis review aims to synthesize evidence on the feasibility, opportunities, and challenges of LLMs across the disease management spectrum, from prevention to screening, diagnosis, treatment, and long-term care. MethodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines, 11 databases (Cochrane Central Register of Controlled Trials, CINAHL, Embase, IEEE Xplore, MEDLINE via Ovid, ProQuest Health & Medicine Collection, ScienceDirect, Scopus, Web of Science Core Collection, China National Knowledge Internet, and SinoMed) were searched on April 17, 2024. Intervention and simulation studies that examined LLMs in the management of chronic diseases were included. The methodological quality of the included studies was evaluated using a rating rubric designed for simulation-based research and the risk of bias in nonrandomized studies of interventions tool for quasi-experimental studies. Narrative analysis with descriptive figures was used to synthesize the study findings. Random-effects meta-analyses were conducted to assess the pooled effect estimates of the feasibility of LLMs in chronic disease management. ResultsA total of 20 studies examined general-purpose (n=17) and retrieval-augmented generation-enhanced LLMs (n=3) for the management of chronic diseases, including cancer, cardiovascular diseases, and metabolic disorders. LLMs demonstrated feasibility across the chronic disease management spectrum by generating relevant, comprehensible, and accurate health recommendations (pooled accurate rate 71%, 95% CI 0.59-0.83; I2=88.32%) with retrieval-augmented generation-enhanced LLMs having higher accuracy rates compared to general-purpose LLMs (odds ratio 2.89, 95% CI 1.83-4.58; I2=54.45%). LLMs facilitated equitable information access; increased patient awareness regarding ailments, preventive measures, and treatment options; and promoted self-management behaviors in lifestyle modification and symptom coping. Additionally, LLMs facilitate compassionate emotional support, social connections, and health care resources to improve the health outcomes of chronic diseases. However, LLMs face challenges in addressing privacy, language, and cultural issues; undertaking advanced tasks, including diagnosis, medication, and comorbidity management; and generating personalized regimens with real-time adjustments and multiple modalities. ConclusionsLLMs have demonstrated the potential to transform chronic disease management at the individual, social, and health care levels; however, their direct application in clinical settings is still in its infancy. A multifaceted approach that incorporates robust data security, domain-specific model fine-tuning, multimodal data integration, and wearables is crucial for the evolution of LLMs into invaluable adjuncts for health care professionals to transform chronic disease management. Trial RegistrationPROSPERO CRD42024545412; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024545412https://www.jmir.org/2025/1/e70535 |
| spellingShingle | Caixia Li Yina Zhao Yang Bai Baoquan Zhao Yetunde Oluwafunmilayo Tola Carmen WH Chan Meifen Zhang Xia Fu Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review Journal of Medical Internet Research |
| title | Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review |
| title_full | Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review |
| title_fullStr | Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review |
| title_full_unstemmed | Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review |
| title_short | Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review |
| title_sort | unveiling the potential of large language models in transforming chronic disease management mixed methods systematic review |
| url | https://www.jmir.org/2025/1/e70535 |
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