Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Since the advent of artificial intelligence (AI) since 1955, the applications of artificial intelligence (AI) have risen over the years. However, there has been little interest in AI in medical education until the last two decades, with an increase in the number of publications and citations on the field. Currently to our knowledge, limited articles are discussing or reviewing the current use of AI in medical education. Objective: This study aims to review the current applications of AI in medical education, as well as the challenges of implementing AI in medical education. Methods: Medline (Ovid), EBSCOhost ERIC & Education Source and Web of Science were searched with explicit inclusion and exclusion criteria. Selected articles were analyzed in full-text using the Extension of Technology Acceptance Model (ETAM) and the Diffusions of Innovations (DOI) theory. Data was subsequently pooled together and analyzed quantitatively. Results: A total of 34 articles were identified; three primary uses of AI in medical education were identified: 1 on curriculum review, 29 on learning support, and four on assessment of students' learning. Thirty-one articles described the challenges of implementation of AI in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education, and technical challenges while developing the AI system. Conclusions: The primary use of AI in medical education was on learning support due to mainly its ability to provide individualized feedback. Little emphasis is placed on curriculum review and assessment of students' learning due to the lack of digitalization and sensitive nature of examinations respectively. Methodological improvements are required to increase the adoption of AI by addressing the technical difficulties of creating an AI system and using novel methods to assess the effectiveness of AI.

There is a lifetime risk of 15% to 25% of development of diabetic foot ulcers (DFUs) in patients with diabetes mellitus. DFUs need to be followed up on and assessed for development of complications and/or resolution, which was traditionally performed using manual measurement. Our study aims to compare the intra‐ and inter‐rater reliability of an artificial intelligence‐enabled wound imaging mobile application (CARES4WOUNDS [C4W] system, Tetsuyu, Singapore) with traditional measurement. This is a prospective cross‐sectional study on 28 patients with DFUs from June 2020 to January 2021. The main wound parameters assessed were length and width. For traditional manual measurement, area was calculated by overlaying traced wound on graphical paper. Intra‐ and inter‐rater reliability was analysed using intra‐class correlation statistics. A value of 0.9 indicates poor, moderate, good, and excellent reliability, respectively. Seventy‐five wound episodes from 28 patients were collected and a total of 547 wound images were analysed in this study. The median wound area during the first clinic consultation and all wound episodes was 3.75 cm² (interquartile range [IQR] 1.40–16.50) and 3.10 cm² (IQR 0.60–14.84), respectively. There is excellent intra‐rater reliability of C4W on three different image captures of the same wound (intra‐rater reliability ranging 0.933–0.994). There is also excellent inter‐rater reliability between three C4W devices for length (0.947), width (0.923), and area (0.965). Good inter‐rater reliability for length, width, and area (range 0.825–0.934) was obtained between wound nurse measurement and each of the C4W devices. In conclusion, we obtained good inter‐rater and intra‐rater reliability of C4W measurements against traditional wound measurement. The C4W is a useful adjunct in monitoring DFU wound progress.

Pyogenic liver abscess (PLA) is a common cause of hepatobiliary sepsis. Klebsiella pneumoniae (KP) is the most common organism causing PLA. Evidence is scarce on the demographics, radiological findings, and outcomes of KPPLA versus non-KPPLA (N-KPPLA). PubMed, Embase, The Cochrane Library, and Scopus were systematically searched until 14 May 2022 for studies comparing KPPLA and N-KPPLA. Exclusion criteria were single-arm studies. Primary outcomes were mortality (30-day/in-hospital) and metastatic complications. There were 16 studies, including 5127 patients (KPPLA n = 3305, N-KPPLA n = 1822). Patients with KPPLA were younger (mean difference: −2.04 years, p = 0.02). History of hepatobiliary disease (Odds ratio (OR) 0.30, 95% CI: 0.20, 0.46) and malignancy (OR 0.26, 95% CI: 0.16, 0.42) were less common in KPPLA. KPPLA was associated with lower incidence of multiple abscesses (OR 0.52, 95% CI: 0.35, 0.76, p < 0.001) and bilobar abscesses (OR 0.60, 95% CI: 0.49, 0.74, p < 0.001). KPPLA has higher overall metastatic complications (KPPLA 9.7% vs. N-KPPLA 4.8%, OR 3.16, 95% CI: 2.00, 4.99, p < 0.001), but lower mortality (KPPLA 3.9% vs. N-KPPLA 7.6%, OR 0.51, 95% CI: 0.34, 0.78, p < 0.001). Trial sequential analysis showed conclusive evidence that KPPLA has lower mortality than N-KPPLA. In conclusion, KPPLA has lower mortality than N-KPPLA.

Diagnosis, severity stratification and management of adult acute pancreatitis–current evidence and controversies