The Philosophy of Expertise in the Age of Medical Informatics: How Healthcare Technology is Transforming Our Understanding of Expertise and Expert Knowledge?

Andrews, R., Mehta, N., Maypole, J., & Martin, S. (2017). Staying afloat in a sea of information: Point-of-care resources. Cleveland Clinic Journal of Medicine, 84(3), 225–235. https://doi.org/10.3949/ccjm.84a.1508710.3949/ccjm.84a.15087Search in Google Scholar

Bedi, G., Carrillo, F., Cecchi, G., Slezak, D., Sigman, M., & Mota, N. et al. (2015). Automated analysis of free speech predicts psychosis onset in high-risk youths. Npj Schizophrenia, 1(1). https://doi.org/10.1038/npjschz.2015.3010.1038/npjschz.2015.30Search in Google Scholar

Buch, V., Ahmed, I., & Maruthappu, M. (2018). Artificial intelligence in medicine: current trends and future possibilities. British Journal of General Practice, 68(668), 143–144. https://doi.org/10.3399/bjgp18x69521310.3399/bjgp18X695213Search in Google Scholar

Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., & Beenen, M. et al. (2010). Predicting protein structures with a multiplayer online game. Nature, 466(7307), 756–760. https://doi.org/10.1038/nature0930410.1038/nature09304Search in Google Scholar

Del Giudice, M. (2018). Evolutionary psychopathology: A unified approach. New York: Oxford University Press.10.1093/med-psych/9780190246846.001.0001Search in Google Scholar

Doraiswamy, P., Blease, C., & Bodner, K. (2020). Artificial intelligence and the future of psychiatry: Insights from a global physician survey. Artificial Intelligence in Medicine, 102, 101753. https://doi.org/10.1016/j.artmed.2019.10175310.1016/j.artmed.2019.101753Search in Google Scholar

Esteva, A., Kuprel, B., Novoa, R., Ko, J., Swetter, S., Blau, H., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature2105610.1038/nature21056Search in Google Scholar

Halligan, S., & Plumb, A. (2019). Re: machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification. Clinical Radiology, 74(2), 161. https://doi.org/10.1016/j.crad.2018.11.01010.1016/j.crad.2018.11.010Search in Google Scholar

Langlotz, C. (2019). Will Artificial Intelligence Replace Radiologists? Radiology: Artificial Intelligence, 1(3), e190058. https://doi.org/10.1148/ryai.201919005810.1148/ryai.2019190058Search in Google Scholar

Lucas, G., Gratch, J., King, A., & Morency, L. (2014). It’s only a computer: Virtual humans increase willingness to disclose. Computers in Human Behavior, 37, 94–100. https://doi.org/10.1016/j.chb.2014.04.04310.1016/j.chb.2014.04.043Search in Google Scholar

Matuchansky, C. (2019). Deep medicine, artificial intelligence, and the practising clinician. The Lancet, 394(10200), 736. https://doi.org/10.1016/s0140-6736(19)31235–810.1016/S0140-6736(19)31235-8Search in Google Scholar

Meyer, A., Longhurst, C., & Singh, H. (2016). Crowdsourcing Diagnosis for Patients With Undiagnosed Illnesses: An Evaluation of CrowdMed. Journal of Medical Internet Research, 18(1), e12. https://doi.org/10.2196/jmir.488710.2196/jmir.4887Search in Google Scholar

Or, F., Torous, J., & Onnela, J. (2017). High potential but limited evidence: Using voice data from smartphones to monitor and diagnose mood disorders. Psychiatric Rehabilitation Journal, 40(3), 320–324. https://doi.org/10.1037/prj000027910.1037/prj0000279Search in Google Scholar

Paparrizos, J., White, R. and Horvitz, E., 2016. Screening for Pancreatic Adenocarcinoma Using Signals From Web Search Logs: Feasibility Study and Results. Journal of Oncology Practice, 12(8), pp. 737–744. https://doi.org/10.1200/JOP.2015.01050410.1200/JOP.2015.010504Search in Google Scholar

Rodríguez Torres, A., Jarillo Soto, E., & Casas Patińo, D. (2018). Medical consultation, time and duration. Medwave, 18(05), e7264–e7264. https://doi.org/10.5867/medwave.2018.05.726410.5867/medwave.2018.05.7264Search in Google Scholar

Schnyer, D., Clasen, P., Gonzalez, C., & Beevers, C. (2017). Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Research: Neuroimaging, 264, 1–9. https://doi.org/10.1016/j.pscychresns.2017.03.00310.1016/j.pscychresns.2017.03.003Search in Google Scholar

Stacewicz, P. (2016). Informational Worldview. Scientific Foundations, and Philosophical Perspectives. Studies In Logic, Grammar And Rhetoric, 48(1), 35–47. https://doi.org/10.1515/slgr-2016-005410.1515/slgr-2016-0054Search in Google Scholar

Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York: Basic Books.Search in Google Scholar

Tzur Bitan, D., Grossman Giron, A., Alon, G., Mendlovic, S., Bloch, Y., & Segev, A. (2018). Attitudes of mental health clinicians toward perceived inaccuracy of a schizophrenia diagnosis in routine clinical practice. BMC Psychiatry, 18(1). https://doi.org/10.1186/s12888-018-1897-210.1186/s12888-018-1897-2Search in Google Scholar

Van Such, M., Lohr, R., Beckman, T., & Naessens, J. (2017). Extent of diagnostic agreement among medical referrals. Journal of Evaluation in Clinical Practice, 23(4), 870–874. https://doi.org/10.1111/jep.1274710.1111/jep.12747Search in Google Scholar

van Wyk, M., & van Ryneveld, L. (2018). Affordances of mobile devices and note-taking apps to support cognitively demanding note-taking. Education And Information Technologies, 23(4), 1639–1653. https://doi.org/10.1007/s10639-017-9684-010.1007/s10639-017-9684-0Search in Google Scholar