Background While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities. Methods We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API. Results Our search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%). Interpretation U.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity.

Objective The objective of this study was to evaluate the effect of skipping breakfast on body composition and cardiometabolic risk factors. Methods This study conducted a systematic review and meta‐analysis of randomized controlled trials (RCTs) evaluating breakfast skipping compared with breakfast consumption. Inclusion criteria included age ≥ 18, intervention duration ≥ 4 weeks, ≥ 7 participants per group, and ≥ 1 body composition measure. Random‐effects meta‐analyses of the effect of breakfast skipping on body composition and cardiometabolic risk factors were performed. Results Seven RCTs (n = 425 participants) with an average duration of 8.6 weeks were included. Compared with breakfast consumption, breakfast skipping significantly reduced body weight (weighted mean difference [WMD] = −0.54 kg [95% CI: −1.05 to −0.03], P = 0.04, I2 = 21.4%). Percent body fat was reported in 5 studies and was not significantly different between breakfast skippers and consumers. Three studies reported on low‐density lipoprotein cholesterol (LDL), which was increased in breakfast skippers as compared with breakfast consumers (WMD = 9.24 mg/dL [95% CI: 2.18 to 16.30], P = 0.01). Breakfast skipping did not lead to significant differences in blood pressure, total cholesterol, high‐density lipoprotein (HDL) cholesterol, triglycerides, C‐reactive protein, insulin, fasting glucose, leptin, homeostatic model assessment of insulin resistance, or ghrelin. Conclusions Breakfast skipping may have a modest impact on weight loss and may increase LDL in the short term. Further studies are needed to provide additional insight into the effects of breakfast skipping.

The effect of emergency department (ED) length of stay (EDLOS) on in-hospital mortality (IHM) remains unclear. The aim of this systematic review and meta-analysis was to determine the association between EDLOS and IHM. We searched the PubMed, Medline, Embase, Web of Science, Cochrane Controlled Register of Trials, CINAHL, PsycInfo, and Scopus databases from their inception until 14–15 January 2022. We included studies reporting the association between EDLOS and IHM. A total of 11,337 references were identified, and 52 studies (total of 1,718,518 ED patients) were included in the systematic review and 33 in the meta-analysis. A statistically significant association between EDLOS and IHM was observed for EDLOS over 24 h in patients admitted to an intensive care unit (ICU) (OR = 1.396, 95% confidence interval [CI]: 1.147 to 1.701; p < 0.001, I2 = 0%) and for low EDLOS in non-ICU-admitted patients (OR = 0.583, 95% CI: 0.453 to 0.745; p < 0.001, I2 = 0%). No associations were detected for the other cut-offs. Our findings suggest that there is an association between IHM low EDLOS and EDLOS exceeding 24 h and IHM. Long stays in the ED should not be allowed and special attention should be given to patients admitted after a short stay in the ED.