Feasibility and safety of using an automated decision support system for insulin therapy in the treatment of steroid‐induced hyperglycemia in patients with acute graft‐versus‐host disease: A randomized trial

Abstract Steroid‐induced hyperglycemia (SIHG) has shown to independently increase the risk for mortality in patients with acute graft‐versus‐host disease, and it is still unclear whether SIHG might be a modifiable risk factor. Therefore, a feasibility trial was carried out aiming to evaluate the performance of a standardized decision support system (GlucoTab [GT]) for insulin therapy in patients with SIHG. A total of 10 hyperglycemic acute graft‐versus‐host disease patients were included and treated either with GT or standard of care during hospitalization. Follow‐up duration was 6 months. Comparing the GT versus standard of care group, 364 versus 1,020 glucose readings were available during a median of 41 days (interquartile range [IQR] 22–89) and 101 days (IQR 55–147) of hospitalization. The median overall glucose levels were 151 mg/dL (123–192) versus 162 mg/dL (IQR 138–193) for GT and standard of care, respectively (P < 0.001); hypoglycemia rates were comparably low. Treatment of SIHG with an algorithm‐based system for subcutaneous insulin was feasible and safe

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Abstract Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic