Foundation models for generalist medical artificial intelligence

Nature, Год журнала: 2023, Номер 616(7956), С. 259 - 265

Опубликована: Апрель 12, 2023

The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities medicine. We propose a new paradigm for medical AI, which we refer as generalist AI (GMAI). GMAI will be capable carrying out diverse set tasks using very little or no task-specific labelled data. Built through self-supervision on large, datasets, flexibly interpret different combinations modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs text. Models turn produce expressive outputs such free-text explanations, spoken recommendations image annotations that demonstrate advanced reasoning abilities. Here identify high-impact potential applications and lay specific technical training datasets necessary enable them. expect GMAI-enabled challenge current strategies regulating validating devices medicine shift practices associated with the collection large datasets.

Язык: Английский

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Scientific discovery in the age of artificial intelligence

Nature, Год журнала: 2023, Номер 620(7972), С. 47 - 60

Опубликована: Авг. 2, 2023

Язык: Английский

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The next generation of evidence-based medicine

Nature Medicine, Год журнала: 2023, Номер 29(1), С. 49 - 58

Опубликована: Янв. 1, 2023

Язык: Английский

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Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine

EBioMedicine, Год журнала: 2023, Номер 90, С. 104512 - 104512

Опубликована: Март 15, 2023

Язык: Английский

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The shaky foundations of large language models and foundation models for electronic health records

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

Опубликована: Июль 29, 2023

The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar for electronic medical records (EMRs) to improve patient care hospital operations. However, recent hype obscured critical gaps our understanding these models' capabilities. In this narrative review, we examine 84 trained on non-imaging EMR data (i.e., clinical text and/or structured data) create a taxonomy delineating their architectures, training data, potential use cases. We find that most are small, narrowly-scoped datasets (e.g., MIMIC-III) or broad, public biomedical corpora PubMed) evaluated tasks do not provide meaningful insights usefulness health systems. Considering findings, propose an improved evaluation framework measuring the benefits is more closely grounded metrics matter healthcare.

Язык: Английский

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Thermal immuno-nanomedicine in cancer

Nature Reviews Clinical Oncology, Год журнала: 2023, Номер 20(2), С. 116 - 134

Опубликована: Янв. 5, 2023

Язык: Английский

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Multimodal data fusion for cancer biomarker discovery with deep learning

Nature Machine Intelligence, Год журнала: 2023, Номер 5(4), С. 351 - 362

Опубликована: Апрель 6, 2023

Язык: Английский

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A physicochemical-sensing electronic skin for stress response monitoring

Nature Electronics, Год журнала: 2024, Номер 7(2), С. 168 - 179

Опубликована: Янв. 19, 2024

Язык: Английский

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Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers

Digital Health, Год журнала: 2023, Номер 9

Опубликована: Янв. 1, 2023

The utilization of artificial intelligence (AI) in clinical practice has increased and is evidently contributing to improved diagnostic accuracy, optimized treatment planning, patient outcomes. rapid evolution AI, especially generative AI large language models (LLMs), have reignited the discussions about their potential impact on healthcare industry, particularly regarding role providers. Concerning questions, “can replace doctors?” “will doctors who are using those not it?” been echoed. To shed light this debate, article focuses emphasizing augmentative healthcare, underlining that aimed complement, rather than replace, fundamental solution emerges with human–AI collaboration, which combines cognitive strengths providers analytical capabilities AI. A human-in-the-loop (HITL) approach ensures systems guided, communicated, supervised by human expertise, thereby maintaining safety quality services. Finally, adoption can be forged further organizational process informed HITL improve multidisciplinary teams loop. create a paradigm shift complementing enhancing skills providers, ultimately leading service quality, outcomes, more efficient system.

Язык: Английский

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Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Annals of Oncology, Год журнала: 2023, Номер 35(1), С. 29 - 65

Опубликована: Окт. 23, 2023

Язык: Английский

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