På denne siden følger en liste over de mest relevante vitenskapelige publikasjonene som SPKI har bidratt til.
2024
Diaz-Asper, C., Hauglid, M. K., Chandler, C., Cohen, A. S., Foltz, P. W., & Elvevåg, B. (2024). A framework for language technologies in behavioral research and clinical applications: Ethical challenges, implications, and solutions. American Psychologist, 79(1), 79–91
Mevik, K., Woldaregay, A. Z., Ringdal, A., Mikalsen, K. Ø., & Xu, Y. (2024). Exploring surgical infection prediction: A comparative study of established risk indexes and a novel model. International Journal of Medical Informatics, 105370.
Chomutare, T., Lamproudis, A., Budrionis, A., Svenning, T. O., Hind, L. I., Ngo, P. D., Mikalsen, K. Ø, Dalianis, H. (2024). Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial. JMIR Research Protocols, 13(1), e54593.
Larsen, M., Olstad, C. F., Mikalsen, K. Ø., Hofvind, S. et al (2024). Performance of an AI System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway. Radiology: Artificial Intelligence, e230375
Fredriksen, H., Burman, P. J., Woldaregay, A. Z., Mikalsen, K. O., & Nymo, S. (2024). Categorization of phenotype trajectories utilizing transformers on clinical time-series. In Proceedings of the 2024 9th International Conference on Machine Learning Technologies (pp. 311-316).
2023
Larsen, M., Olstad, C. F., Koch, H. W., Martiniussen, M. A., Hoff, S. R., Lund-Hanssen, H., Mikalsen, K. Ø., … & Hofvind, S. (2023). AI risk score on screening mammograms preceding breast cancer diagnosis. Radiology, 309(1), e230989.
Hauglid, M. K., & Mahler, T. (2023). Doctor Chatbot: The EUʼs Regulatory Prescription for Generative Medical AI. Oslo Law Review, (1), 1-23.
Radiya, K., Joakimsen, H. L., Mikalsen, K. Ø., Aahlin, E. K., Lindsetmo, R. O., & Mortensen, K. E. (2023). Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. European Radiology, 1-29.
Wickstrøm, K. K., Østmo, E. A., Radiya, K., Mikalsen, K. Ø., Kampffmeyer, M. C., & Jenssen, R. (2023). A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. Computerized Medical Imaging and Graphics, 107, 102239.
JA Lund, KØ Mikalsen, J Burman, AZ Woldaregay, R Jenssen (2023). Instruction-guided deidentification with synthetic test cases for Norwegian clinical text, Northern Lights Deep Learning Conference 2024
2022
Hauglid, M. K., & Mikalsen, K. Ø. (2022). Tilgang til helseopplysninger i maskinlæringsprosjekter. Lov og Rett, (7), 419-439.
2021
Boubekki, A., Myhre, J. N., Luppino, L. T., Mikalsen, K. Ø., Revhaug, A., & Jenssen, R. (2021). Clinically relevant features for predicting the severity of surgical site infections. IEEE Journal of Biomedical and Health Informatics, 26(4), 1794-1801.
Wickstrøm, K., Mikalsen, K. Ø., Kampffmeyer, M., Revhaug, A., & Jenssen, R. (2020). Uncertainty-aware deep ensembles for reliable and explainable predictions of clinical time series. IEEE Journal of Biomedical and Health Informatics, 25(7), 2435-2444.
