The Impact of Expectation Management and Model Transparency on Radiologists' Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study.

Background: Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems.

Objective: We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists' trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans.

Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions.

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy.

Looking through the eyes of the multidisciplinary team: the design and clinical evaluation of a decision support system for lung cancer care.

Background: Decision-making in lung cancer is complex due to a rapidly increasing amount of diagnostic data and treatment options. The need for timely and accurate diagnosis and delivery of care demands high-quality multidisciplinary team (MDT) collaboration and coordination. Clinical decision support systems (CDSSs) can potentially support MDTs in constructing a shared mental model of a patient case.

Immersive training: breaking the bubble and measuring the heat.

Background: Minimal access surgery and, lately, single-incision laparoscopic procedures are challenging and demanding with regard to the skills of the surgeon performing the procedures. This article presents the results of an investigation of the performance and attention focus of 21 medical interns and surgical residents training in an immersive context. That is, training 'in situation', representing more realistically the demands imposed on the surgeons during minimal access surgery.

Do absorption and realistic distraction influence performance of component task surgical procedure?

Background: Surgeons perform complex tasks while exposed to multiple distracting sources that may increase stress in the operating room (e.g., music, conversation, and unadapted use of sophisticated technologies).