...enable the deconstruction of neural networks to make the features that drive the AI performance understandable for clinicians. ...allow clinicians to retrain AI models with local data if the needs ...of their patients and hospital require it. If combined with privacy-preserving computing tools, such as federated learning, open-source AI could further remove barriers for the fast scalability of home-grown AI solutions developed in hospitals across the health system while maintaining clinician and patient trust in the ownership and regulation of data.
On May 21, 1999, Merck was granted approval by the Food and Drug Administration (FDA) to market rofecoxib (Vioxx). On September 30, 2004, after more than 80 million patients had taken this medicine ...and annual sales had topped $2.5 billion, the company withdrew the drug because of an excess risk of myocardial infarctions and strokes. This represents the largest prescription-drug withdrawal in history, but had the many warning signs along the way been heeded, such a debacle could have been prevented.
Neither of the two major forces in this five-and-a-half-year affair — neither Merck nor the FDA — fulfilled its . . .
Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases ...using medical imaging.
In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase, Science Citation Index, and Conference Proceedings Citation Index for studies published from Jan 1, 2012, to June 6, 2019. Studies comparing the diagnostic performance of deep learning models and health-care professionals based on medical imaging, for any disease, were included. We excluded studies that used medical waveform data graphics material or investigated the accuracy of image segmentation rather than disease classification. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. Studies undertaking an out-of-sample external validation were included in a meta-analysis, using a unified hierarchical model. This study is registered with PROSPERO, CRD42018091176.
Our search identified 31 587 studies, of which 82 (describing 147 patient cohorts) were included. 69 studies provided enough data to construct contingency tables, enabling calculation of test accuracy, with sensitivity ranging from 9·7% to 100·0% (mean 79·1%, SD 0·2) and specificity ranging from 38·9% to 100·0% (mean 88·3%, SD 0·1). An out-of-sample external validation was done in 25 studies, of which 14 made the comparison between deep learning models and health-care professionals in the same sample. Comparison of the performance between health-care professionals in these 14 studies, when restricting the analysis to the contingency table for each study reporting the highest accuracy, found a pooled sensitivity of 87·0% (95% CI 83·0-90·2) for deep learning models and 86·4% (79·9-91·0) for health-care professionals, and a pooled specificity of 92·5% (95% CI 85·1-96·4) for deep learning models and 90·5% (80·6-95·7) for health-care professionals.
Our review found the diagnostic performance of deep learning models to be equivalent to that of health-care professionals. However, a major finding of the review is that few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample. Additionally, poor reporting is prevalent in deep learning studies, which limits reliable interpretation of the reported diagnostic accuracy. New reporting standards that address specific challenges of deep learning could improve future studies, enabling greater confidence in the results of future evaluations of this promising technology.
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In the 1980s, when I did emergency coronary angiograms for patients with acute myocardial infarction, I marvelled at how the ECG accurately predicted the infarct-related artery and whether the ...occlusion was proximal or distal. By contrast, accuracy in predicting atrial fibrillation for a given individual could incentivise a person to reduce modifiable risk factors, such as reduction of excessive alcohol intake or bodyweight. Deep learning of ECGs exemplifies the double-edged sword of artificial intelligence in medicine: it has the potential to ameliorate diagnoses while at the same time promote misuse. Since we are still in the early days of applying deep neural networks to ECGs, and most other medical scans and images, it is worth considering all the things we could detect with machine help but haven't yet attempted.
Faith Hark/Scripps Research Translational Institute Research by one of us (ZO), with colleagues, has produced an algorithm trained to predict the knee pain reported by the patient, rather than the ...x-ray interpretation of the doctor. In this study, we replicated clinical guidelines for eligibility for knee replacement surgeries but replaced the radiologist's judgment with the algorithm's severity score. ...by training algorithms to predict labels related to clinical outcomes, rather than doctors' judgments, we can start to push forward a new kind of clinical science.
Public statements about Covid that a Stanford faculty member made in 2020 reinvigorated debate over academic freedom and institutional obligations, particularly in cases where public health is at ...stake.
Nonetheless, such research provides a potential roadmap for early detection of pancreatic cancer that extends beyond the current narrow definition of HRIs by enriching the general population with a ...larger proportion of individuals at “sporadic” risk who are identified through the mining of EHR data (figure). With such surveillance of a high-risk group, early diagnosis would be enabled, as would the potential for improving outcomes, with treatment including surgical resection followed by emerging immunotherapy options, such as personalised vaccines. AM is a consultant for Tezcat Biosciences, is listed as an inventor on a patent licensed to Thrive Earlier Detection (an Exact Sciences Company) relevant to early detection of pancreatic cancer, and is supported by the Sheikh Khalifa bin Zayed Foundation.
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