Briganti, G., Le Moine, O., Artificial intelligence in medicine: today and tomorrow. Front Med, 7, 2020, 27.
Smuha, N., A definition of AI [internet]. 2018, European Commission [Disponible sur : https://ec.europa.eu/futurium/en/system/files/ged/ai_hleg_definition_of_ai_18_december_1.pdf].
Turing, A.M., Computing machinery and intelligence. Mind, 59, 1950, 433.
Ciardo, F., De Tommaso, D., Wykowska, A., Human-like behavioral variability blurs the distinction between a human and a machine in a nonverbal Turing test. Sci Robot, 7, 2022, eabo1241.
Russell, S., Norvig, P., Artificial intelligence: a modern approach. 2002.
Topalovic, M., Das, N., Burgel, P.R., et al. Pulmonary function study investigators; pulmonary function study investigators: artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J, 53, 2019, 1801660.
Ding, M.Q., Chen, L., Cooper, G.F., et al. Precision oncology beyond targeted therapy: combining omics data with machine learning matches the majority of cancer cells to effective therapeutics. Mol Cancer Res MCR 16 (2018), 269–278.
Noble, W.S., What is a support vector machine?. Nat Biotechnol 24 (2006), 1565–1567.
Suthaharan, S., Decision Tree Learning. Suthaharan, S., (eds.) Machine learning models and algorithms for big data classification: thinking with examples for effective learning [internet]., 2016, Springer US, Boston, MA, 237–269 [cited 2022 Dec 5. Integrated Series in Information Systems. Disponible sur : https://doi.org/10.1007/978-1-4899-7641-3_10].
Breiman, L., Random forests. Mach Learn 45 (2001), 5–32.
Rudin, C., Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1 (2019), 206–215.
Regalia, G., Onorati, F., Lai, M., et al. Multimodal wrist-worn devices for seizure detection and advancing research: focus on the Empatica wristbands. Epilepsy Res 153 (2019), 79–82.
Lawton, J., Blackburn, M., Allen, J., et al. Patients’ and caregivers’ experiences of using continuous glucose monitoring to support diabetes self-management: qualitative study. BMC Endocr Disord, 18, 2018 [cited 2019 Dec 14. Disponible sur : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819241/].
Christiansen, M.P., Garg, S.K., Brazg, R., et al. Accuracy of a fourth-generation subcutaneous continuous glucose sensor. Diabetes Technol Ther 19 (2017), 446–456.
