AI in health and medicine

Nature Medicine, Год журнала: 2022, Номер 28(1), С. 31 - 38

Опубликована: Янв. 1, 2022

Язык: Английский

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The false hope of current approaches to explainable artificial intelligence in health care

The Lancet Digital Health, Год журнала: 2021, Номер 3(11), С. e745 - e750

Опубликована: Окт. 25, 2021

The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable used in high-stakes scenarios such as medicine. It been argued that will engender trust with the health-care workforce, provide transparency into decision making process, and potentially mitigate various kinds bias. In this Viewpoint, we argue argument represents a false hope for explainability methods are unlikely achieve these goals patient-level support. We an overview techniques highlight how failure cases can cause problems individual patients. absence suitable methods, advocate rigorous internal external validation models more direct means achieving often associated explainability, caution against having requirement clinically deployed models.

Язык: Английский

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ChatGPT and Other Large Language Models Are Double-edged Swords

Radiology, Год журнала: 2023, Номер 307(2)

Опубликована: Янв. 26, 2023

Язык: Английский

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Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

Medical Image Analysis, Год журнала: 2022, Номер 79, С. 102470 - 102470

Опубликована: Май 4, 2022

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially high-stakes decision making areas as medical image analysis. This survey presents overview eXplainable Artificial Intelligence (XAI) used A framework XAI criteria is introduced to classify analysis methods. Papers on techniques are then surveyed and categorized according anatomical location. The paper concludes with outlook future opportunities

Язык: Английский

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Transformers in medical imaging: A survey

Medical Image Analysis, Год журнала: 2023, Номер 88, С. 102802 - 102802

Опубликована: Апрель 5, 2023

Язык: Английский

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Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities

Knowledge-Based Systems, Год журнала: 2023, Номер 263, С. 110273 - 110273

Опубликована: Янв. 11, 2023

learning Deep Meta-survey Responsible AI a b s t r c tThe past decade has seen significant progress in artificial intelligence (AI), which resulted algorithms being adopted for resolving variety of problems.However, this success been met by increasing model complexity and employing black-box models that lack transparency.In response to need, Explainable (XAI) proposed make more transparent thus advance the adoption critical domains.Although there are several reviews XAI topics literature have identified challenges potential research directions XAI, these scattered.This study, hence, presents systematic meta-survey future organized two themes: (1) general (2) based on machine life cycle's phases: design, development, deployment.We believe our contributes providing guide exploration area.

Язык: Английский

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Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology

Canadian Journal of Cardiology, Год журнала: 2021, Номер 38(2), С. 204 - 213

Опубликована: Сен. 14, 2021

Many clinicians remain wary of machine learning because longstanding concerns about “black box” models. “Black is shorthand for models that are sufficiently complex they not straightforwardly interpretable to humans. Lack interpretability in predictive can undermine trust those models, especially health care, which so many decisions are— literally—life and death issues. There has been a recent explosion research the field explainable aimed at addressing these concerns. The promise considerable, but it important cardiologists who may encounter techniques clinical decision-support tools or novel papers have critical understanding both their strengths limitations. This paper reviews key concepts as apply cardiology. Key reviewed include vs explainability global local explanations. Techniques demonstrated permutation importance, surrogate decision trees, model-agnostic explanations, partial dependence plots. We discuss several limitations with techniques, focusing on how nature explanations approximations omit information black-box work why make certain predictions. conclude by proposing rule thumb when appropriate use black- box rather than

Язык: Английский

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Artificial intelligence for multimodal data integration in oncology

Cancer Cell, Год журнала: 2022, Номер 40(10), С. 1095 - 1110

Опубликована: Окт. 1, 2022

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in realm single modality, neglecting broader clinical context, which inevitably diminishes their potential. Integration different data modalities provides opportunities increase robustness accuracy diagnostic prognostic models, bringing AI closer practice. are also capable discovering novel patterns within across suitable for explaining differences outcomes or treatment resistance. The insights gleaned such can guide exploration studies contribute discovery biomarkers therapeutic targets. To support these advances, here we present synopsis methods strategies multimodal fusion association discovery. We outline approaches interpretability directions AI-driven through interconnections. examine challenges adoption discuss emerging solutions.

Язык: Английский

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Artificial intelligence and machine learning for medical imaging: A technology review

Physica Medica, Год журнала: 2021, Номер 83, С. 242 - 256

Опубликована: Март 1, 2021

Язык: Английский

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U-Net-Based Medical Image Segmentation

Journal of Healthcare Engineering, Год журнала: 2022, Номер 2022, С. 1 - 16

Опубликована: Апрель 15, 2022

Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed 2015 shows the advantages of accurate small targets and its scalable network architecture. With increasing requirements for performance imaging recent years, cited academically more than 2500 times. Many scholars have constantly developing This paper summarizes image technologies based on structure variants concerning their structure, innovation, efficiency, etc.; reviews categorizes related methodology; introduces loss functions, evaluation parameters, modules commonly imaging, which will provide a good reference future research.

Язык: Английский

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