Professor & Consultant, CAMES.
Main area of research: How to support clinicians' learning and performances through technologies such as Artificial Intelligence (AI).
Deputy Editor of 'Advances in Health Sciences Education'
Specialty: Obstetrics & Gynaecology
No. of publications: 90
Keywords: Artificial Intelligence in Medical Education, Competency-Based Medical Education, Assessment, Transfer of Learning.
Martin Tolsgaard's research aims at linking training and assessment with quality of care. He examines how to improve skills transfer from the simulated to the clinical setting using patient-level data as well as how to develop methods and systems for competency-based medical education (CBME). In doing this, he has a special focus on how to provide low-cost high-value medical education.
Ongoing research includes using big data and artificial intelligence models to help design decision-support systems that can facilitate learning, automatize assessments, and improve performances with patients.
Noerholk LM, Tolsgaard MG. Structural individualism or collaborative mindsets: Next steps for peer learning. Med Educ. 2021 Dec 30.
Johnsson V, Tolsgaard M, Hyett J, Gembruch U, Windrim R, Khalil A, Tiblad E, Slaghekke F, Paladini D, Nayahangan L, Sundberg KM, Nørgaard LN, Petersen OB. Consensus on Training and Assessment of Competence in Performing Chorionic Villus Sampling and Amniocentesis: An International Delphi Survey. Fetal Diagn Ther. 2021;48(10):720-737
PhD students (Medical Education)