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As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16 hi CD66b lo neutrophil and IFN-γ + granzyme B + Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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ImportanceDespite the potential of machine learning to improve multiple aspects of patient care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a prerequisite to large-scale clinical adoption of an intervention, and important questions remain regarding how machine learning interventions are being incorporated into clinical trials in health care.ObjectiveTo systematically examine the design, reporting standards, risk of bias, and inclusivity of RCTs for medical machine learning interventions.Evidence ReviewIn this systematic review, the Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection online databases were searched and citation chasing was done to find relevant articles published from the inception of each database to October 15, 2021. Search terms for machine learning, clinical decision-making, and RCTs were used. Exclusion criteria included implementation of a non-RCT design, absence of original data, and evaluation of nonclinical interventions. Data were extracted from published articles. Trial characteristics, including primary intervention, demographics, adherence to the CONSORT-AI reporting guideline, and Cochrane risk of bias were analyzed.FindingsLiterature search yielded 19 737 articles, of which 41 RCTs involved a median of 294 participants (range, 17-2488 participants). A total of 16 RCTS (39%) were published in 2021, 21 (51%) were conducted at single sites, and 15 (37%) involved endoscopy. No trials adhered to all CONSORT-AI standards. Common reasons for nonadherence were not assessing poor-quality or unavailable input data (38 trials [93%]), not analyzing performance errors (38 [93%]), and not including a statement regarding code or algorithm availability (37 [90%]). Overall risk of bias was high in 7 trials (17%). Of 11 trials (27%) that reported race and ethnicity data, the median proportion of participants from underrepresented minority groups was 21% (range, 0%-51%).Conclusions and RelevanceThis systematic review found that despite the large number of medical machine learning–based algorithms in development, few RCTs for these technologies have been conducted. Among published RCTs, there was high variability in adherence to reporting standards and risk of bias and a lack of participants from underrepresented minority groups. These findings merit attention and should be considered in future RCT design and reporting.

This article has an accompanying continuing medical education activity, also eligible for MOC credit, on page e93. Learning Objective-Upon completion of this activity, successful learners will be able to explain the optimal diagnostic evaluation of a patient presenting with acute lower gastrointestinal bleeding. BACKGROUND & AIMS: Guidelines recommend colonoscopy evaluation within 24 hours of presentation or admission in patients with high-risk or severe acute lower gastrointestinal bleeding (LGIB). Meta-analyses of the timing of colonoscopy have relied primarily on observational studies that had major potential for bias. We performed a systematic review of randomized trials to determine optimal timing of colonoscopy for patients hospitalized with acute LGIB. METHODS: We searched publication databases through July 2019 and abstracts from gastroenterology meetings through November 2019 for randomized trials of patients with acute LGIB or hematochezia. We searched for studies that compared early colonoscopy (within 24 hours) with elective colonoscopy beyond 24 hours and/or other diagnostic tests. Our primary outcome was further bleeding, defined as persistent or recurrent bleeding after index examination. Secondary outcomes included mortality, diagnostic yield (identifying source of bleeding), endoscopic intervention, and any primary hemostatic intervention (endoscopic, surgical, or interventional radiologic). We performed dual independent review, data extraction, and risk of bias assessments. We performed the meta-analysis using a random-effects model. RESULTS: Our final analysis included data from 4 randomized trials. Further bleeding was not decreased among patients who received early vs later, elective colonoscopy (relative risk [RR] for further bleeding with early colonoscopy, 1.57; 95% CI. 0.74-3.31). We did not find significant differences in the secondary outcomes of mortality (RR, 0.

Machine learning, a subset of artificial intelligence (AI), is a set of computational tools that can be used to enhance provision of clinical care in all areas of medicine. Gastroenterology and hepatology utilize multiple sources of information, including visual findings on endoscopy, radiologic imaging, manometric testing, genomes, proteomes, and metabolomes. However, clinical care is complex and requires a thoughtful approach to best deploy AI tools to improve quality of care and bring value to patients and providers. On the operational level, AI‐assisted clinical management should consider logistic challenges in care delivery, data management, and algorithmic stewardship. There is still much work to be done on a broader societal level in developing ethical, regulatory, and reimbursement frameworks. A multidisciplinary approach and awareness of AI tools will create a vibrant ecosystem for using AI‐assisted tools to guide and enhance clinical practice. From optically enhanced endoscopy to clinical decision support for risk stratification, AI tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time.

Elderly patients with IBD are at an increased risk of hospital- and therapy-related complications. We found a paucity of high-quality studies evaluating outcomes in elderly patients with IBD. Further studies of elderly patients with IBD are needed to further evaluate the effect of age on medical and surgical complications.

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