Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.

Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.

Dealing with cancer screening in the COVID-19 era.

OBJECTIVE: This article aims to alert health professionals for cancer screening in the face of the possibility of new waves of disease. METHODS: A narrative review was conducted through a search in MEDLINE, Lilacs, Chinese Biomedical Literature Database, and international medical societies publications. RESULTS: Breast cancer: in high-risk patients (confirmed familial cancer syndrome or with high-risk tools scores), clinicians should act according to usual recommendations; in average-risk individuals, consider screening with mammography with a longer time span (maximum of two years). Cervical cancer: women turning 25 years old who have already been immunized and with no previous Pap test can have the test postponed during the pandemic; if there is no previous dose of Human Papillomavirus vaccination, initiation of screening should be recommended following a more rigid approach for COVID prevention; in women over 30 years of age who have never participated in cervical screening, the first screening exam is also essential. Colorectal cancer: if the individual is at elevated risk for familial cancer, the screening with colonoscopy according to usual recommendations should be supported; if at average risk consider screening with Fecal Occult Blood Test. Prostate cancer: there is a trend to postpone routine prostate cancer screening until the pandemic subsides. CONCLUSIONS: The decision to keep cancer screening must be discussed and individualized, considering the possibility of new waves of COVID-19.