AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.

INTRODUCTION: A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. METHODS AND ANALYSIS: A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers' performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. ETHICS AND DISSEMINATION: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT06018545.

Learning together: clinical skills teaching for medical and nursing students.

OBJECTIVE: To evaluate the feasibility and effectiveness of shared learning of clinical skills for medical and nursing students at the University of Manchester. DESIGN: Medical and nursing students learned clinical skills in either uniprofessional or multiprofessional groups. These groups rotated through skills stations taught by multiprofessional facilitators. The groups stayed together for a series of 3 sessions held at weekly intervals (an induction meeting followed by 2 3-hour teaching sessions). Both quantitative and qualitative methods were used to evaluate the project. CONTEXT: A total of 113 Year 3 students registered on the Medical (n = 41), Bachelor of Nursing (Honours) (n = 43) or the Diploma for Professional Studies in Nursing (n = 29) courses participated in the project which was set in the clinical skills unit of a teaching hospital. RESULTS: Pre- and post self-evaluation of confidence levels for the taught skills revealed a statistically significant increase for all skills. The primary reason students gave for participation in the project was to learn or consolidate skills. An additional inducement for participation was the opportunity to share knowledge and observations between professional groups. Tutors also evaluated the experience favourably, particularly with regard to small group discussions. They indicated that the programme provided an opportunity to standardise clinical skills teaching. CONCLUSION: Collaborative learning opportunities for nursing and medical students are feasible and add value to the learning experience. Data indicate positive outcomes of learning in multiprofessional groups, comprising increased confidence levels, increased understanding of others' professional roles and personal development.