Machine learning in clinical decision making

Adlung, Lorenz; Cohen, Y.; Mor, Uria; Elinav, Eran

doi:10.1016/j.medj.2021.04.006

Cited by 85 publications

(56 citation statements)

References 199 publications

(217 reference statements)

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“…Ideally, the importance of explainable models will increase uptake and help providers make more informed decisions. 105 Models with limited interpretability, like neural networks, were relied upon most frequently. Though machine-learningbased diagnostics are becoming increasingly accurate, reliance on models that cannot be fully understood is of growing concern 95,106 This review was limited to inclusion criteria that may not be representative of the entire breath of machine learning's integration into health care.…”

Section: Diversity In Machine Learning Fitzsimmons Et Al 578mentioning

confidence: 99%

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

2022

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Objective As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations. Methods We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded. Results From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% (N = 34) included the sex ratio of the patient population. Discussion With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training. Conclusion As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.

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Section: Diversity In Machine Learning Fitzsimmons Et Al 578mentioning

confidence: 99%

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

2022

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“…For these reasons, the FDA has approved the use of specific DL-driven diagnostic computational tools for clinical usage (Table 3) [124][125][126]. The application of AI encompasses several medical and biomedical fields, including radiology [127], gastroenterology [128,129], neurology [130,131], ophthalmology [132,133], cardiology [134,135], dermatology [136], general pathology [137], oncology [138], healthcare [139,140], and clinical medicine [141,142].…”

Section: General Considerationmentioning

confidence: 99%

Artificial Intelligence in Translational Medicine

Brogi

Calderone

2021

IJTM

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The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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“…general pathology [127], oncology [128], healthcare [129,130] and clinical medicine [131,132]. Abbreviation: AD -Alzheimer disease; ADHD -attention deficit hyperactivity disorder; AIartificial intelligence; ANN -artificial neural network; ASD -autism spectrum disorder; BISbioimpedance spectroscopy; DL -deep learning; CNN -convolutional neural network; CT -computed tomography; DBT -digital breast tomosynthesis; EEG -electroencephalogram; ECG -electrocardiogram; LVO -large vessel occlusion; MCI -mild cognitive impairment: ML -machine learning; MRCP -magnetic resonance cholangiopancreatography; MRI -magnetic resonance imaging; OARs -organs-at-risk; OSA -obstructive sleep apnea; PET -positron emission tomography; PNX -pneumothorax.…”

Section: General Considerationmentioning

confidence: 99%

Artificial Intelligence in Translational Medicine

Brogi¹,

Calderone²

2021

Preprint

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The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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Machine learning in clinical decision making

Cited by 85 publications

References 199 publications

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

Artificial Intelligence in Translational Medicine

Artificial Intelligence in Translational Medicine

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