Unveiling the potential of large language models in transforming chronic disease management: A mixed-method systematic review (Preprint) DOI Creative Commons
Caixia Li,

Yina Zhao,

Yang Bai

и другие.

Journal of Medical Internet Research, Год журнала: 2024, Номер unknown

Опубликована: Дек. 24, 2024

Chronic diseases are a major global health burden, accounting for nearly three-quarters of the deaths worldwide. Large language models (LLMs) advanced artificial intelligence systems with transformative potential to optimize chronic disease management; however, robust evidence is lacking. This review aims synthesize on feasibility, opportunities, and challenges LLMs across management spectrum, from prevention screening, diagnosis, treatment, long-term care. Following PRISMA (Preferred Reporting Items Systematic Reviews Meta-Analysis) guidelines, 11 databases (Cochrane Central Register Controlled Trials, CINAHL, Embase, IEEE Xplore, MEDLINE via Ovid, ProQuest Health & Medicine Collection, ScienceDirect, Scopus, Web Science Core China National Knowledge Internet, SinoMed) were searched April 17, 2024. Intervention simulation studies that examined in included. The methodological quality included was evaluated using rating rubric designed simulation-based research risk bias nonrandomized interventions tool quasi-experimental studies. Narrative analysis descriptive figures used study findings. Random-effects meta-analyses conducted assess pooled effect estimates feasibility management. A total 20 general-purpose (n=17) retrieval-augmented generation-enhanced (n=3) diseases, including cancer, cardiovascular metabolic disorders. demonstrated spectrum by generating relevant, comprehensible, accurate recommendations (pooled rate 71%, 95% CI 0.59-0.83; I2=88.32%) having higher accuracy rates compared (odds ratio 2.89, 1.83-4.58; I2=54.45%). facilitated equitable information access; increased patient awareness regarding ailments, preventive measures, treatment options; promoted self-management behaviors lifestyle modification symptom coping. Additionally, facilitate compassionate emotional support, social connections, care resources improve outcomes diseases. However, face addressing privacy, language, cultural issues; undertaking tasks, medication, comorbidity personalized regimens real-time adjustments multiple modalities. have transform at individual, social, levels; their direct application clinical settings still its infancy. multifaceted approach incorporates data security, domain-specific model fine-tuning, multimodal integration, wearables crucial evolution into invaluable adjuncts professionals PROSPERO CRD42024545412; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024545412.

Язык: Английский

ChatGPT in veterinary medicine: a practical guidance of generative artificial intelligence in clinics, education, and research DOI Creative Commons
Candice P. Chu

Frontiers in Veterinary Science, Год журнала: 2024, Номер 11

Опубликована: Июнь 7, 2024

ChatGPT, the most accessible generative artificial intelligence (AI) tool, offers considerable potential for veterinary medicine, yet a dedicated review of its specific applications is lacking. This concisely synthesizes latest research and practical ChatGPT within clinical, educational, domains medicine. It intends to provide guidance actionable examples how AI can be directly utilized by professionals without programming background. For practitioners, extract patient data, generate progress notes, potentially assist in diagnosing complex cases. Veterinary educators create custom GPTs student support, while students utilize exam preparation. aid academic writing tasks research, but publishers have set requirements authors follow. Despite transformative potential, careful use essential avoid pitfalls like hallucination. addresses ethical considerations, provides learning resources, tangible guide responsible implementation. A table key takeaways was provided summarize this review. By highlighting benefits limitations, equips veterinarians, educators, researchers harness power effectively.

Язык: Английский

Процитировано

1
Toward Foundation Models in Radiology? Quantitative Assessment of GPT-4V’s Multimodal and Multianatomic Region Capabilities DOI
Quirin Strotzer, Felix Nieberle, Laura S. Kupke

и другие.

Radiology, Год журнала: 2024, Номер 313(2)

Опубликована: Ноя. 1, 2024

OpenAI’s GPT-4V reliably identified the imaging modality and anatomic region but could not safely detect, classify, or rule out abnormalities on single MRI, CT, radiographic images.

Язык: Английский

Процитировано

1
Active Prompting of Vision Language Models for Human-in-the-loop Classification and Explanation of Microscopy Images DOI

Abhiram Kandiyana,

Peter R. Mouton, Lawrence Hall

и другие.

Опубликована: Июнь 26, 2024

Язык: Английский

Процитировано

0
Unveiling the potential of large language models in transforming chronic disease management: A mixed-method systematic review (Preprint) DOI Creative Commons
Caixia Li,

Yina Zhao,

Yang Bai

и другие.

Опубликована: Дек. 24, 2024

BACKGROUND Accounting for nearly three-quarters of deaths worldwide, chronic diseases are a major global health burden. Large language models (LLMs) advanced artificial intelligence systems, possessing transformative potential to optimise disease management, yet robust evidence is lacking. OBJECTIVE To synthesise on the feasibility, opportunities, and challenges LLMs across management spectrum–from prevention screening, diagnosis, treatment, long-term care. METHODS Following PRISMA (Preferred Reporting Items Systematic Reviews Meta-Analysis) guidelines, eleven databases (Cochrane Central Register Controlled Trials, CINAHL, Embase, IEEE Xplore, Medline via Ovid, ProQuest Health & Medicine Collection, ScienceDirect, Scopus, Web Science Core China National Knowledge Internet, SinoMed) were searched 17 April 2024. Intervention simulation studies included if they examined in managing diseases. Narrative analysis with descriptive figures utilised study findings. Random-effects meta-analyses conducted assess pooled effect estimates LLM feasibility management. RESULTS Twenty eligible examining general-purpose (n = 17) fine-tuned 3) diseases, including cancer, cardiovascular metabolic disorders. demonstrated spectrum by generating relevant, comprehensible, accurate recommendations (71%; 95% confidence interval [CI] 0.59, 0.83; I2 88.32%) having higher rates compared (odds ratio 2.89; CI 1.83, 4.58; 54.45%). facilitated equitable information access, increased patient awareness ailments, preventive measures, treatment options, promoted self-management behaviours lifestyle modification symptom coping. Additionally, compassionate emotional support, social connections, healthcare resource improve outcomes However, faced addressing privacy, language, cultural issues, undertaking tasks, diagnostic, medication, comorbidities personalised regimens real-time adjustments multiple modalities. CONCLUSIONS transform at individual, social, levels, their direct application clinical settings still its infancy. A multifaced approach–incorporating data security, domain-specific model fine-tuning, multimodal integration, wearables–is crucial evolve into invaluable adjuncts professionals CLINICALTRIAL PROSPERO (CRD42024545412).

Язык: Английский

Процитировано

0
Unveiling the potential of large language models in transforming chronic disease management: A mixed-method systematic review (Preprint) DOI Creative Commons
Caixia Li,

Yina Zhao,

Yang Bai

и другие.

Journal of Medical Internet Research, Год журнала: 2024, Номер unknown

Опубликована: Дек. 24, 2024

Chronic diseases are a major global health burden, accounting for nearly three-quarters of the deaths worldwide. Large language models (LLMs) advanced artificial intelligence systems with transformative potential to optimize chronic disease management; however, robust evidence is lacking. This review aims synthesize on feasibility, opportunities, and challenges LLMs across management spectrum, from prevention screening, diagnosis, treatment, long-term care. Following PRISMA (Preferred Reporting Items Systematic Reviews Meta-Analysis) guidelines, 11 databases (Cochrane Central Register Controlled Trials, CINAHL, Embase, IEEE Xplore, MEDLINE via Ovid, ProQuest Health & Medicine Collection, ScienceDirect, Scopus, Web Science Core China National Knowledge Internet, SinoMed) were searched April 17, 2024. Intervention simulation studies that examined in included. The methodological quality included was evaluated using rating rubric designed simulation-based research risk bias nonrandomized interventions tool quasi-experimental studies. Narrative analysis descriptive figures used study findings. Random-effects meta-analyses conducted assess pooled effect estimates feasibility management. A total 20 general-purpose (n=17) retrieval-augmented generation-enhanced (n=3) diseases, including cancer, cardiovascular metabolic disorders. demonstrated spectrum by generating relevant, comprehensible, accurate recommendations (pooled rate 71%, 95% CI 0.59-0.83; I2=88.32%) having higher accuracy rates compared (odds ratio 2.89, 1.83-4.58; I2=54.45%). facilitated equitable information access; increased patient awareness regarding ailments, preventive measures, treatment options; promoted self-management behaviors lifestyle modification symptom coping. Additionally, facilitate compassionate emotional support, social connections, care resources improve outcomes diseases. However, face addressing privacy, language, cultural issues; undertaking tasks, medication, comorbidity personalized regimens real-time adjustments multiple modalities. have transform at individual, social, levels; their direct application clinical settings still its infancy. multifaceted approach incorporates data security, domain-specific model fine-tuning, multimodal integration, wearables crucial evolution into invaluable adjuncts professionals PROSPERO CRD42024545412; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024545412.

Язык: Английский

Процитировано

0