Development and Assessment of an Artificial Intelligence-Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices.

Importance: Most dermatologic cases are initially evaluated by nondermatologists such as primary care physicians (PCPs) or nurse practitioners (NPs). Objective: To evaluate an artificial intelligence (AI)-based tool that assists with diagnoses of dermatologic conditions. Design, Setting, and Participants: This multiple-reader, multiple-case diagnostic study developed an AI-based tool and evaluated its utility. Primary care physicians and NPs retrospectively reviewed an enriched set of cases representing 120 different skin conditions. Randomization was used to ensure each clinician reviewed each case either with or without AI assistance; each clinician alternated between batches of 50 cases in each modality. The reviews occurred from February 21 to April 28, 2020. Data were analyzed from May 26, 2020, to January 27, 2021. Exposures: An AI-based assistive tool for interpreting clinical images and associated medical history. Main Outcomes and Measures: The primary analysis evaluated agreement with reference diagnoses provided by a panel of 3 dermatologists for PCPs and NPs. Secondary analyses included diagnostic accuracy for biopsy-confirmed cases, biopsy and referral rates, review time, and diagnostic confidence. Results: Forty board-certified clinicians, including 20 PCPs (14 women [70.0%]; mean experience, 11.3 [range, 2-32] years) and 20 NPs (18 women [90.0%]; mean experience, 13.1 [range, 2-34] years) reviewed 1048 retrospective cases (672 female [64.2%]; median age, 43 [interquartile range, 30-56] years; 41 920 total reviews) from a teledermatology practice serving 11 sites and provided 0 to 5 differential diagnoses per case (mean [SD], 1.6 [0.7]). The PCPs were located across 12 states, and the NPs practiced in primary care without physician supervision across 9 states. The NPs had a mean of 13.1 (range, 2-34) years of experience and practiced in primary care without physician supervision across 9 states. Artificial intelligence assistance was significantly associated with higher agreement with reference diagnoses. For PCPs, the increase in diagnostic agreement was 10% (95% CI, 8%-11%; P < .001), from 48% to 58%; for NPs, the increase was 12% (95% CI, 10%-14%; P < .001), from 46% to 58%. In secondary analyses, agreement with biopsy-obtained diagnosis categories of maglignant, precancerous, or benign increased by 3% (95% CI, -1% to 7%) for PCPs and by 8% (95% CI, 3%-13%) for NPs. Rates of desire for biopsies decreased by 1% (95% CI, 0-3%) for PCPs and 2% (95% CI, 1%-3%) for NPs; the rate of desire for referrals decreased by 3% (95% CI, 1%-4%) for PCPs and NPs. Diagnostic agreement on cases not indicated for a dermatologist referral increased by 10% (95% CI, 8%-12%) for PCPs and 12% (95% CI, 10%-14%) for NPs, and median review time increased slightly by 5 (95% CI, 0-8) seconds for PCPs and 7 (95% CI, 5-10) seconds for NPs per case. Conclusions and Relevance: Artificial intelligence assistance was associated with improved diagnoses by PCPs and NPs for 1 in every 8 to 10 cases, indicating potential for improving the quality of dermatologic care.

Effect of Pregame Training Tutorials on Player Performance in Kinect-Based Health Games.

OBJECTIVE: The objective of this study was to examine the influence of pregame tutorials on player performance in Kinect(®) (Microsoft(®), Redmond, WA)-based health games and to determine whether the additional exertion caused by longer playtime introduced by the tutorial would influence in-game performance. MATERIALS AND METHODS: Thirty-two college-age test participants were randomly assigned to two groups: pregame training tutorial and pregame verbal instructions. The variable of interest was the degree to which players were able to accurately perform a series of static arm gestures in order to duplicate specific reference poses. Participants played a Kinect-based bird simulator game called "Burnie" that required the player to hold specific, static poses that correspond to physical therapy treatment actions. This study is a pilot conducted in advance of testing the game with its target audience of pediatric cerebral palsy patients. RESULTS: Analysis of variance did not detect a significant difference in pose accuracy between the tutorial and nontutorial groups. However, the tutorial group's pose accuracy was higher on average than that of the nontutorial group. CONCLUSIONS: Given the limited sample size in this study, it cannot be stated with certainty that greater pose accuracy will occur if a pregame tutorial level is administered. However, a trend was observed along most measures that the tutorial group achieved greater accuracy scores than the verbal instruction group. Further study with greater statistical power is strongly recommended.

Coordination of multiple services for a patient with severe lymphedema of the right lower extremity.

Surgical resection of the right lower extremity in an adult with severe lymph-edema of the extremity required intensive coordination of multiple services and specialists because of the medical and surgical complexity of the patient's condition, massive limb size, and surgical location in a children's hospital. Early and prolonged planning was necessary to anticipate problems and provide optimal care. The patient had a successful surgical outcome, improvement in medical comorbid conditions postoperatively, and improvement in the quality of life upon discharge. This coordinated effort will be used in the future as a template for patients with complex conditions whose care requires lengthy planning and involves multiple services and specialists.