Creation and Adoption of Large Language Models in Medicine

JAMA, Год журнала: 2023, Номер 330(9), С. 866 - 866

Опубликована: Авг. 7, 2023

There is increased interest in and potential benefits from using large language models (LLMs) medicine. However, by simply wondering how the LLMs applications powered them will reshape medicine instead of getting actively involved, agency shaping these tools can be used lost.Applications are increasingly to perform medical tasks without underlying model being trained on records verifying their purported benefit performing those tasks.The creation use need shaped provisioning relevant training data, specifying desired benefits, evaluating via testing real-world deployments.

Язык: Английский

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A study of generative large language model for medical research and healthcare

npj Digital Medicine, Год журнала: 2023, Номер 6(1)

Опубликована: Ноя. 16, 2023

Abstract There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions based on general-purpose LLMs such as ChatGPT, which not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 from 126 departments approximately 2 million patients at the University Florida Health (2) 195 diverse general English text. We train GatorTronGPT GPT-3 architecture with up 20 parameters evaluate its utility biomedical natural processing (NLP) healthcare generation. improves processing. apply generate synthetic Synthetic NLP trained generated by outperform real-world Physicians’ Turing test 1 (worst) 9 (best) scale shows that there no significant differences linguistic readability ( p = 0.22; 6.57 compared 6.93 human) relevance 0.91; 7.0 6.97 physicians cannot differentiate them < 0.001). provides insights into opportunities challenges research

Язык: Английский

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Adapted large language models can outperform medical experts in clinical text summarization

Nature Medicine, Год журнала: 2024, Номер 30(4), С. 1134 - 1142

Опубликована: Фев. 27, 2024

Язык: Английский

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Large Language Models in Medicine: The Potentials and Pitfalls

Annals of Internal Medicine, Год журнала: 2024, Номер 177(2), С. 210 - 220

Опубликована: Янв. 29, 2024

Large language models (LLMs) are artificial intelligence trained on vast text data to generate humanlike outputs. They have been applied various tasks in health care, ranging from answering medical examination questions generating clinical reports. With increasing institutional partnerships between companies producing LLMs and systems, the real-world application of these is nearing realization. As gain traction, care practitioners must understand what are, their development, current potential applications, associated pitfalls a setting. This review, coupled with tutorial, provides comprehensive yet accessible overview areas aim familiarizing professionals rapidly changing landscape medicine. Furthermore, authors highlight active research field that promise improve LLMs' usability contexts.

Язык: Английский

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A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges

Diagnostics, Год журнала: 2024, Номер 14(1), С. 109 - 109

Опубликована: Янв. 4, 2024

Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by generating human-like text through prompts. ChatGPT’s adaptability holds promise for reshaping medical practices, improving patient care, enhancing interactions among healthcare professionals, patients, data. In pandemic management, rapidly disseminates vital information. It serves virtual assistant surgical consultations, aids dental simplifies education, disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment medicine, G2: buildings equipment, G3: parts the human body areas disease, G4: G5: citizens, G6: cellular imaging, radiology, pulse images, G7: doctors nurses, G8: tools, devices administration. Balancing role with judgment remains challenge. systematic literature review using PRISMA approach explored healthcare, highlighting versatile applications, limitations, motivation, challenges. conclusion, diverse applications demonstrate its innovation, serving valuable resource students, academics, researchers Additionally, this study guide, assisting field alike.

Язык: Английский

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Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation

Clinical Infectious Diseases, Год журнала: 2023, Номер 78(4), С. 860 - 866

Опубликована: Ноя. 16, 2023

Abstract Large language models (LLMs) are artificial intelligence systems trained by deep learning algorithms to process natural and generate text responses user prompts. Some approach physician performance on a range of medical challenges, leading some proponents advocate for their potential use in clinical consultation prompting consternation about the future cognitive specialties. However, LLMs currently have limitations that preclude safe deployment performing specialist consultations, including frequent confabulations, lack contextual awareness crucial nuanced diagnostic treatment plans, inscrutable unexplainable training data methods, propensity recapitulate biases. Nonetheless, considering rapid improvement this technology, growing calls integration, healthcare chronically undervalue specialties, it is critical infectious diseases clinicians engage with enable informed advocacy how they should—and shouldn’t—be used augment care.

Язык: Английский

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Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Окт. 30, 2023

Abstract Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural processing (NLP) tasks, efficacy diverse range of clinical summarization tasks has not yet been rigorously demonstrated. In this work, we apply domain adaptation methods to eight LLMs, spanning six datasets four distinct tasks: radiology reports, patient questions, progress notes, doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between addition instances where recent advances LLMs may improve results. Further, reader study with ten physicians, show that summaries our best-adapted are preferable human terms completeness correctness. ensuing qualitative analysis highlights challenges faced by both experts. Lastly, correlate traditional NLP metrics scores enhance understanding these align physician preferences. research marks the first evidence outperforming experts text across multiple tasks. This implies integrating into workflows could alleviate documentation burden, empowering focus more personalized care inherently aspects medicine.

Язык: Английский

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Evaluating large language models as agents in the clinic

npj Digital Medicine, Год журнала: 2024, Номер 7(1)

Опубликована: Апрель 3, 2024

Recent developments in large language models (LLMs) have unlocked opportunities for healthcare, from information synthesis to clinical decision support. These LLMs are not just capable of modeling language, but can also act as intelligent "agents" that interact with stakeholders open-ended conversations and even influence decision-making. Rather than relying on benchmarks measure a model's ability process data or answer standardized test questions, LLM agents be modeled high-fidelity simulations settings should assessed their impact workflows. evaluation frameworks, which we refer "Artificial Intelligence Structured Clinical Examinations" ("AI-SCE"), draw comparable technologies where machines operate varying degrees self-governance, such self-driving cars, dynamic environments multiple stakeholders. Developing these robust, real-world evaluations will crucial towards deploying medical settings.

Язык: Английский

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Testing and Evaluation of Health Care Applications of Large Language Models

JAMA, Год журнала: 2024, Номер unknown

Опубликована: Окт. 15, 2024

Importance Large language models (LLMs) can assist in various health care activities, but current evaluation approaches may not adequately identify the most useful application areas. Objective To summarize existing evaluations of LLMs terms 5 components: (1) data type, (2) task, (3) natural processing (NLP) and understanding (NLU) tasks, (4) dimension evaluation, (5) medical specialty. Data Sources A systematic search PubMed Web Science was performed for studies published between January 1, 2022, February 19, 2024. Study Selection Studies evaluating 1 or more care. Extraction Synthesis Three independent reviewers categorized via keyword searches based on used, NLP NLU dimensions Results Of 519 reviewed, 2024, only 5% used real patient LLM evaluation. The common tasks were assessing knowledge such as answering licensing examination questions (44.5%) making diagnoses (19.5%). Administrative assigning billing codes (0.2%) writing prescriptions less studied. For focused question (84.2%), while summarization (8.9%) conversational dialogue (3.3%) infrequent. Almost all (95.4%) accuracy primary evaluation; fairness, bias, toxicity (15.8%), deployment considerations (4.6%), calibration uncertainty (1.2%) infrequently measured. Finally, specialty area, generic applications (25.6%), internal medicine (16.4%), surgery (11.4%), ophthalmology (6.9%), with nuclear (0.6%), physical (0.4%), genetics being least represented. Conclusions Relevance Existing mostly focus examinations, without consideration data. Dimensions received limited attention. Future should adopt standardized metrics, use clinical data, broaden to include a wider range specialties.

Язык: Английский

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Generative AI and large language models in health care: pathways to implementation

npj Digital Medicine, Год журнала: 2024, Номер 7(1)

Опубликована: Март 7, 2024

Generative AI is designed to create new content from trained parameters. Learning large amounts of data, many these models aim simulate human conversation. being applied different sectors. Within healthcare there has been innovation specifically towards generative on electronic medical record data. A recent review characterizes models, their strengths, and weaknesses. Inspired by that work, we present our evaluation checklist for records.

Язык: Английский

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