Foundation models for generalist medical artificial intelligence

Nature, Journal Year: 2023, Volume and Issue: 616(7956), P. 259 - 265

Published: April 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.

Language: Английский

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A foundation model for generalizable disease detection from retinal images

Nature, Journal Year: 2023, Volume and Issue: 622(7981), P. 156 - 163

Published: Sept. 13, 2023

Abstract Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis eye diseases systemic disorders 1 . However, development AI models requires substantial annotation are usually task-specific with limited generalizability to different clinical applications 2 Here, we present RETFound, a foundation model that learns generalizable representations from unlabelled provides basis label-efficient adaptation several applications. Specifically, RETFound is trained on 1.6 million by means self-supervised learning then adapted disease detection tasks explicit labels. We show consistently outperforms comparison prognosis sight-threatening diseases, as well incident prediction complex such heart failure myocardial infarction fewer labelled data. solution improve performance alleviate workload experts enable broad imaging.

Language: Английский

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The Current and Future State of AI Interpretation of Medical Images

New England Journal of Medicine, Journal Year: 2023, Volume and Issue: 388(21), P. 1981 - 1990

Published: May 24, 2023

The authors examine the advantages and limitations of current clinical radiologic AI systems, new workflows, potential effect generative large multimodal foundation models.

Language: Английский

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Self-supervised learning for medical image classification: a systematic review and implementation guidelines

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: April 26, 2023

Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare patient outcomes. However, the prevailing paradigm of training models requires large quantities labeled data, which is both time-consuming cost-prohibitive to curate images. Self-supervised has potential make significant contributions development robust imaging through its ability learn useful insights from copious datasets without labels. In this review, we consistent descriptions different self-supervised strategies compose a systematic review papers published between 2012 2022 on PubMed, Scopus, ArXiv that applied classification. We screened total 412 relevant studies included 79 data extraction analysis. With comprehensive effort, synthesize collective knowledge prior work implementation guidelines future researchers interested applying their classification models.

Language: Английский

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A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 242, P. 122807 - 122807

Published: Dec. 2, 2023

Deep learning has emerged as a powerful tool in various domains, revolutionising machine research. However, one persistent challenge is the scarcity of labelled training data, which hampers performance and generalisation deep models. To address this limitation, researchers have developed innovative methods to overcome data enhance model capabilities. Two prevalent techniques that gained significant attention are transfer self-supervised learning. Transfer leverages knowledge learned from pre-training on large-scale dataset, such ImageNet, applies it target task with limited data. This approach allows models benefit representations effectively new tasks, resulting improved generalisation. On other hand, focuses using pretext tasks do not require manual annotation, allowing them learn valuable large amounts unlabelled These can then be fine-tuned for downstream mitigating need extensive In recent years, found applications fields, including medical image processing, video recognition, natural language processing. approaches demonstrated remarkable achievements, enabling breakthroughs areas disease diagnosis, object understanding. while these offer numerous advantages, they also limitations. For example, may face domain mismatch issues between requires careful design ensure meaningful representations. review paper explores fields within past three years. It delves into advantages limitations each approach, assesses employing techniques, identifies potential directions future By providing comprehensive current methods, article offers guidance selecting best technique specific issue.

Language: Английский

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Artificial intelligence for digital and computational pathology

Nature Reviews Bioengineering, Journal Year: 2023, Volume and Issue: 1(12), P. 930 - 949

Published: Oct. 2, 2023

Language: Английский

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A guide to artificial intelligence for cancer researchers

Nature reviews. Cancer, Journal Year: 2024, Volume and Issue: 24(6), P. 427 - 441

Published: May 16, 2024

Language: Английский

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Ten deep learning techniques to address small data problems with remote sensing

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 125, P. 103569 - 103569

Published: Nov. 18, 2023

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited training DL models, especially when these models represent key socio-environmental problems, such as the monitoring extreme, destructive climate events, biodiversity, sudden changes in ecosystem states. Such cases, also known small pose significant methodological challenges. This review summarises challenges RS domain possibility using emerging techniques to overcome them. We show that problem common challenge across disciplines scales results poor model generalisability transferability. then introduce an overview ten promising techniques: transfer learning, self-supervised semi-supervised few-shot zero-shot active weakly supervised multitask process-aware ensemble learning; we include validation technique spatial k-fold cross validation. Our particular contribution was develop flowchart helps users select which use given by answering few questions. hope our article facilitate applications tackle societally important environmental problems with reference data.

Language: Английский

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Battery safety: Machine learning-based prognostics

Progress in Energy and Combustion Science, Journal Year: 2024, Volume and Issue: 102, P. 101142 - 101142

Published: Jan. 19, 2024

Lithium-ion batteries play a pivotal role in wide range of applications, from electronic devices to large-scale electrified transportation systems and grid-scale energy storage. Nevertheless, they are vulnerable both progressive aging unexpected failures, which can result catastrophic events such as explosions or fires. Given their expanding global presence, the safety these potential hazards serious malfunctions now major public concerns. Over past decade, scholars industry experts intensively exploring methods monitor battery safety, spanning materials cell, pack system levels across various spectral, spatial, temporal scopes. In this Review, we start by summarizing mechanisms nature failures. Following this, explore intricacies predicting evolution delve into specialized knowledge essential for data-driven, machine learning models. We offer an exhaustive review spotlighting latest strides fault diagnosis failure prognosis via array approaches. Our discussion encompasses: (1) supervised reinforcement integrated with models, apt faults/failures probing causes protocols at cell level; (2) unsupervised, semi-supervised, self-supervised learning, advantageous harnessing vast data sets modules/packs; (3) few-shot tailored gleaning insights scarce examples, alongside physics-informed bolster model generalization optimize training data-scarce settings. conclude casting light on prospective horizons comprehensive, real-world prognostics management.

Language: Английский

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Toward Explainable Artificial Intelligence for Precision Pathology

Annual Review of Pathology Mechanisms of Disease, Journal Year: 2023, Volume and Issue: 19(1), P. 541 - 570

Published: Oct. 23, 2023

The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect its ability analyze histological images and increasingly large molecular profiling data a quantitative, integrative, standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential facilitate complex analysis tasks, including clinical, histological, for disease classification; tissue biomarker quantification; clinical outcome prediction. This review provides general introduction AI describes developments focus on applications beyond. We explain limitations black-box character conventional describe solutions make machine decisions transparent so-called explainable AI. purpose is foster mutual understanding both biomedical side. To that end, addition providing an overview relevant foundations learning, we present worked-through examples better practical what can achieve how it should be done.

Language: Английский

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