Understanding natural language: Potential application of large language models to ophthalmology

Asia-Pacific Journal of Ophthalmology, Год журнала: 2024, Номер 13(4), С. 100085 - 100085

Опубликована: Июль 1, 2024

Large language models (LLMs), a natural processing technology based on deep learning, are currently in the spotlight. These closely mimic comprehension and generation. Their evolution has undergone several waves of innovation similar to convolutional neural networks. The transformer architecture advancement generative artificial intelligence marks monumental leap beyond early-stage pattern recognition via supervised learning. With expansion parameters training data (terabytes), LLMs unveil remarkable human interactivity, encompassing capabilities such as memory retention comprehension. advances make particularly well-suited for roles healthcare communication between medical practitioners patients. In this comprehensive review, we discuss trajectory their potential implications clinicians For clinicians, can be used automated documentation, given better inputs extensive validation, may able autonomously diagnose treat future. patient care, triage suggestions, summarization documents, explanation patient's condition, customizing education materials tailored level. limitations possible solutions real-world use also presented. Given rapid advancements area, review attempts briefly cover many that play ophthalmic space, with focus improving quality delivery.

Язык: Английский

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Large Language Model Prompting Techniques for Advancement in Clinical Medicine

Journal of Clinical Medicine, Год журнала: 2024, Номер 13(17), С. 5101 - 5101

Опубликована: Авг. 28, 2024

Large Language Models (LLMs have the potential to revolutionize clinical medicine by enhancing healthcare access, diagnosis, surgical planning, and education. However, their utilization requires careful, prompt engineering mitigate challenges like hallucinations biases. Proper of LLMs involves understanding foundational concepts such as tokenization, embeddings, attention mechanisms, alongside strategic prompting techniques ensure accurate outputs. For innovative solutions, it is essential maintain ongoing collaboration between AI technology medical professionals. Ethical considerations, including data security bias mitigation, are critical application. By leveraging supplementary resources in research education, we can enhance learning support knowledge-based inquiries, ultimately advancing quality accessibility care. Continued development necessary fully realize transforming healthcare.

Язык: Английский

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The Role of Prompt Engineering for Multimodal LLM Glaucoma Diagnosis

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 1, 2024

Abstract Background and Aim This study evaluates the diagnostic performance of multimodal large language models (LLMs), GPT-4o Claude Sonnet 3.5, in detecting glaucoma from fundus images. We specifically assess impact prompt engineering use reference images on model performance. Methods utilized ACRIMA public dataset, comprising 705 labeled images, designed four types, ranging simple instructions to more refined prompts with The two were tested across 5640 API runs, accuracy, sensitivity, specificity, PPV, NPV assessed through non-parametric statistical tests. Results 3.5 achieved a highest sensitivity 94.92%, specificity 73.46%, F1 score 0.726. reached 81.47%, 50.49%, 0.645. incorporation improved GPT-4o’s accuracy by 39.8% 3.5’s 64.2%, significantly enhancing both models’ Conclusion Multimodal LLMs demonstrated potential diagnosing glaucoma, achieving far exceeding 22% reported for primary care physicians literature. Prompt engineering, especially As become integrated into medical practice, efficient design may be key, training doctors these tools effectively could enhance clinical outcomes.

Язык: Английский

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Use of AI in family medicine publications: a joint editorial from journal editors

Evidence-Based Practice, Год журнала: 2025, Номер 28(1), С. 1 - 4

Опубликована: Янв. 1, 2025

Schrager, Sarina MD, MS; Seehusen, Dean A. MPH; Sexton, Sumi M. MD; Richardson, Caroline Neher, Jon Pimlott, Nicholas Bowman, Marjorie Rodíguez, José Morley, Christopher P. PhD; Li, Li PhD, Dera, James Dom MD Author Information

Язык: Английский

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Use of AI in Family Medicine Publications: A Joint Editorial From Journal Editors

Family Medicine, Год журнала: 2025, Номер 57(1), С. 1 - 5

Опубликована: Янв. 13, 2025

Язык: Английский

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Use of AI in Family Medicine Publications: A Joint Editorial From Journal Editors

PRiMER, Год журнала: 2025, Номер 9

Опубликована: Янв. 13, 2025

There are multiple guidelines from publishers and organizations on the use of artiXcial intelligence (AI) in publishing.However, none speciXc to family medicine.Most journals have some basic AI recommendations for authors, but more explicit direction is needed, as not all tools same.

Язык: Английский

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Use of AI in family medicine publications: a joint editorial from journal editors

Family Medicine and Community Health, Год журнала: 2025, Номер 13(1), С. e003238 - e003238

Опубликована: Янв. 1, 2025

There are multiple guidelines from publishers and organisations on the use of artificial intelligence (AI) in publishing.[1–5][1] However, none specific to family medicine. Most journals have some basic AI recommendations for authors, but more explicit direction is needed, as not all

Язык: Английский

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Use of AI in Family Medicine Publications: A Joint Editorial From Journal Editors

The Annals of Family Medicine, Год журнала: 2025, Номер unknown, С. 240575 - 240575

Опубликована: Янв. 13, 2025

2][3][4][5] However, none are specific to family medicine.Most journals have some basic AI use recommendations for authors, but more explicit direction is needed, as not all tools the same.As medicine journal editors, we want provide a unified statement about in academic publishing publishers, and peer reviewers based on our current understanding of field.The technology advancing rapidly.While text generated from early large language models (LLMs) was relatively easy identify, newer versions getting progressively better at imitating human challenging detect.Our goal develop framework managing journals.As this rapidly evolving environment, acknowledge that any such will need continue evolve.However, also feel it important guidance where today.Definitions: Artificial intelligence broad field computers perform tasks historically been thought require intelligence.LLMs recent breakthrough allow generate seems like comes human.LLMs deal with generation, while broader term generative can include images or figures.Chat GPT one earliest widely used LLM models, other companies developed similar products.LLMs "learn" do multifaceted analysis word sequences massive training database new words using complex probability model.The model has random component, so responses exact same prompt submitted multiple times be identical.LLMs looks medical article response prompt, article's content may accurate.LLMs "confabulate" generating convincing includes false information. 6,7,8LLMs search internet answers questions.However, they paired engines increasingly sophisticated ways.For rest editorial, synonymously LLMs.

Язык: Английский

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The Role of Large Language Model Chatbots in Sexual Education: An Unmet Need of Research

Journal of Psychosexual Health, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

Large language model (LLM) chatbots have demonstrated significant capability in patient education by offering accessible, consistent, and personalized information. Their ability to interact real-time adapt responses based on user input makes them valuable tools enhancing knowledge engagement. Sexual developing countries faces substantial challenges. Sociocultural barriers, limited access comprehensive educational resources, stigmatization surrounding sexual health contribute inadequate education. Traditional methods often fail reach remote or underserved populations, there is a general shortage of qualified educators resources. Chatbots present promising solution these They can offer anonymous, culturally sensitive information health, overcoming barriers related stigma privacy. While LLM hold potential improve countries, their implementation must be carefully managed address challenges such as ensuring accuracy cultural sensitivity. There dearth research Hence, unmet need the reliability information, maintaining sensitivity, assessing engagement, integration with traditional methods, exploring long-term impact improving knowledge.

Язык: Английский

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Unveiling the potential of large language models in transforming chronic disease management: A mixed-method systematic review (Preprint)

Journal of Medical Internet Research, Год журнала: 2025, Номер 27, С. e70535 - e70535

Опубликована: Март 19, 2025

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

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