A whole-slide foundation model for digital pathology from real-world data

Nature, Journal Year: 2024, Volume and Issue: 630(8015), P. 181 - 188

Published: May 22, 2024

Abstract Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands image tiles 1–3 . Prior models have often resorted to subsampling small portion for each slide, thus missing the important slide-level context 4 Here we present Prov-GigaPath, whole-slide foundation model pretrained on 1.3 billion 256 × in 171,189 whole slides from Providence, large US health network comprising 28 cancer centres. The originated more than 30,000 patients covering 31 major tissue types. To pretrain propose GigaPath, novel vision transformer architecture pretraining slides. scale GigaPath learning with tiles, adapts newly developed LongNet 5 method digital pathology. evaluate construct benchmark 9 subtyping tasks and 17 pathomics tasks, using both Providence TCGA data 6 With large-scale ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance 25 out 26 significant improvement over second-best 18 tasks. We further demonstrate potential vision–language 7,8 by incorporating reports. In sum, is an open-weight that achieves various demonstrating importance real-world modelling.

Language: Английский

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Evaluation and mitigation of the limitations of large language models in clinical decision-making

Nature Medicine, Journal Year: 2024, Volume and Issue: 30(9), P. 2613 - 2622

Published: July 4, 2024

Clinical decision-making is one of the most impactful parts a physician's responsibilities and stands to benefit greatly from artificial intelligence solutions large language models (LLMs) in particular. However, while LLMs have achieved excellent performance on medical licensing exams, these tests fail assess many skills necessary for deployment realistic clinical environment, including gathering information, adhering guidelines, integrating into workflows. Here we created curated dataset based Medical Information Mart Intensive Care database spanning 2,400 real patient cases four common abdominal pathologies as well framework simulate setting. We show that current state-of-the-art do not accurately diagnose patients across all (performing significantly worse than physicians), follow neither diagnostic nor treatment cannot interpret laboratory results, thus posing serious risk health patients. Furthermore, move beyond accuracy demonstrate they be easily integrated existing workflows because often instructions are sensitive both quantity order information. Overall, our analysis reveals currently ready autonomous providing guide future studies.

Language: Английский

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The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives

Journal of Medical Systems, Journal Year: 2024, Volume and Issue: 48(1)

Published: Feb. 17, 2024

Within the domain of Natural Language Processing (NLP), Large Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through scaling model size, pretraining corpora, computational resources. The potential healthcare applications these primarily involve chatbots interaction systems for clinical documentation management, medical literature summarization (Biomedical NLP). challenge in this field lies research diagnostic decision support, as well patient triage. Therefore, LLMs can be used multiple tasks within care, research, education. Throughout 2023, there has been escalation release LLMs, some which applicable domain. This remarkable output is largely effect customization pre-trained like chatbots, virtual assistants, or any system requiring human-like conversational engagement. As professionals, we recognize imperative stay at forefront knowledge. However, keeping abreast rapid evolution technology practically unattainable, and, above all, understanding its limitations remains a subject ongoing debate. Consequently, article aims provide succinct overview recently released emphasizing their use medicine. Perspectives more range safe effective also discussed. upcoming evolutionary leap involves transition from AI-powered designed answering questions versatile practical tool providers such generalist biomedical AI multimodal-based calibrated decision-making processes. On other hand, development accurate partners could enhance engagement, offering personalized improving chronic disease management.

Language: Английский

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On the challenges and perspectives of foundation models for medical image analysis

Medical Image Analysis, Journal Year: 2023, Volume and Issue: 91, P. 102996 - 102996

Published: Oct. 12, 2023

Language: Английский

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

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 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.

Language: Английский

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The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study

The Lancet Digital Health, Journal Year: 2024, Volume and Issue: 6(8), P. e555 - e561

Published: July 24, 2024

BackgroundArtificial intelligence (AI) applications in health care have been effective many areas of medicine, but they are often trained for a single task using labelled data, making deployment and generalisability challenging. How well general-purpose AI language model performs diagnosis triage relative to physicians laypeople is not understood.MethodsWe compared the predictive accuracy Generative Pre-trained Transformer 3 (GPT-3)'s diagnostic ability 48 validated synthetic case vignettes (<50 words; sixth-grade reading level or below) both common (eg, viral illness) severe heart attack) conditions nationally representative sample 5000 lay people from USA who could use internet find correct options 21 practising at Harvard Medical School. There were 12 each four categories: emergent, within one day, 1 week, self-care. The category (ie, ground truth) vignette was determined by two general internists For vignette, human respondents GPT-3 prompted list diagnoses order likelihood, marked as if ground-truth top three listed diagnoses. accuracy, we examined whether respondents' GPT-3's selected exactly according categories, matched dichotomised variable (emergent day vs week self-care). We estimated confidence on given modified bootstrap resampling procedure, how calibrated computing calibration curves Brier scores. also performed subgroup analysis acuity, an error advice characterise its might affect patients this tool decide should seek medical immediately.FindingsAmong all cases, replied with 88% (42/48, 95% CI 75–94) 54% (2700/5000, 53–55) individuals (p<0.0001) 96% (637/666, 94–97) (p=0·012). triaged 70% (34/48, 57–82) versus 74% (3706/5000, 73–75; p=0.60) 91% (608/666, 89–93%; p<0.0001) physicians. As measured score, prediction reasonably (Brier score=0·18) score=0·22). observed inverse relationship between acuity (p<0·0001) fitted trend line –8·33% decrease every increase acuity. analysis, deprioritised truly emergent cases seven instances.InterpretationA without any content-specific training perform levels close to, below, better than individuals. found that performance inferior triage, sometimes large margin, closer Although comparable physicians, it significantly typical person search engine.FundingThe National Heart, Lung, Blood Institute.

Language: Английский

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A generalist vision–language foundation model for diverse biomedical tasks

Nature Medicine, Journal Year: 2024, Volume and Issue: 30(11), P. 3129 - 3141

Published: Aug. 7, 2024

Language: Английский

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Transformers in single-cell omics: a review and new perspectives

Nature Methods, Journal Year: 2024, Volume and Issue: 21(8), P. 1430 - 1443

Published: Aug. 1, 2024

Language: Английский

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Recent Advances in Large Language Models for Healthcare

BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(2), P. 1097 - 1143

Published: April 16, 2024

Recent advances in the field of large language models (LLMs) underline their high potential for applications a variety sectors. Their use healthcare, particular, holds out promising prospects improving medical practices. As we highlight this paper, LLMs have demonstrated remarkable capabilities understanding and generation that could indeed be put to good field. We also present main architectures these models, such as GPT, Bloom, or LLaMA, composed billions parameters. then examine recent trends datasets used train models. classify them according different criteria, size, source, subject (patient records, scientific articles, etc.). mention help improve patient care, accelerate research, optimize efficiency healthcare systems assisted diagnosis. several technical ethical issues need resolved before can extensively Consequently, propose discussion offered by new generations linguistic limitations when deployed domain healthcare.

Language: Английский

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Advancing Chinese biomedical text mining with community challenges

Journal of Biomedical Informatics, Journal Year: 2024, Volume and Issue: 157, P. 104716 - 104716

Published: Aug. 27, 2024

Objective: This study aims to review the recent advances in community challenges for biomedical text mining China.Methods: We collected information of evaluation tasks released mining, including task description, dataset data source, type and related links.A systematic summary comparative analysis were conducted on various natural language processing tasks, such as named entity recognition, normalization, attribute extraction, relation event classification, similarity, knowledge graph construction, question answering, generation, large model evaluation.Results: identified 39 from 6 that spanned 2017 2023.Our revealed diverse range types sources mining.We explored potential clinical applications these challenge a translational informatics perspective.We compared with their English counterparts, discussed contributions, limitations, lessons guidelines challenges, while highlighting future directions era models.Conclusion: Community competitions have played crucial role promoting technology innovation fostering interdisciplinary collaboration field mining.These provide valuable platforms researchers develop state-of-the-art solutions.

Language: Английский

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