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A Collaborative Model for Integration of Artificial Intelligence in Primary Care

Serge Dolgikh

Abstract


The cost of primary care is rapidly increasing in the developed world, and improving the accuracy of screening and diagnostic testing as well as other areas of primary care can be seen as an essential component in ensuring the long-term sustainability of the quality and efficiency of public health care systems. In this study, the authors propose a simple yet robust model of collaborative decision-making incorporating machine and human competences whereby the strengths and advantages of artificial intelligence methods can be harnessed to improve the overall accuracy of essential testing, diagnostics, screening, and other critical areas of patient care while addressing concerns and ensuring safety and complete human control over the course of diagnostics and treatment.

 

Doi: 10.28991/HEF-2021-02-04-07

Full Text: PDF


Keywords


Artificial Intelligence; Diagnostics; Decision Making Models, Human and Machine Intelligences.

References


Kondro, W. (2004). Canadian report quantifies cost of medical errors. Lancet, 363(9426), 2059. doi:10.1016/s0140-6736(04)16492-1.

OECD (2020). The Economics of Patient Safety: From analysis to action. Organisation for Economic Co-operation and Development Report, Paris, France. Available online: https://www.oecd.org/health/health-systems/Economics-of-Patient-Safety-October-2020.pdf (accessed on May 2021).

WHO (2016). Technical Series on Safer Primary Care: Diagnostic errors. World Health Organization. Available online: https://www.who.int/publications/i/item/9789241511636 (accessed on May 2021).

Sidorov, P. I., Solov’ev, A. G., & Novikova, I. A. (2008). The burnout syndrome in communicative professional workers. Gigiena i Sanitariia, 3(3), 29–33.

Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., Ledsam, J. R., Schmid, M. K., Balaskas, K., Topol, E. J., Bachmann, L. M., Keane, P. A., & Denniston, A. K. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271–e297. doi:10.1016/S2589-7500(19)30123-2.

The Guardian (2019). AI equal with human experts in medical diagnosis, study finds | Artificial intelligence (AI). The Guardian News & Media, London, United Kingdom. Available online: https://www.theguardian.com/technology/2019/sep/24/ai-equal-with-human-experts-in-medical-diagnosis-study-finds (accessed on April 2021).

Roberts, J. (2016). Thinking machines: The search for artificial intelligence. Distillations, Science History Institute. Available online: https://www.sciencehistory.org/distillations/thinking-machines-the-search-for-artificial-intelligence (accessed on May 2021).

Kostopoulou, O., Delaney, B. C., & Munro, C. W. (2008). Diagnostic difficulty and error in primary care--a systematic review. Family Practice, 25(6), 400–413. doi:10.1093/fampra/cmn071.

Jain, A., Way, D., Gupta, V., Gao, Y., de Oliveira Marinho, G., Hartford, J., … Liu, Y. (2021). Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices. JAMA Network Open, 4(4), e217249. doi:10.1001/jamanetworkopen.2021.7249.

Miller, S., Gilbert, S., Virani, V., & Wicks, P. (2020). Patients’ Utilization and Perception of an Artificial Intelligence–Based Symptom Assessment and Advice Technology in a British Primary Care Waiting Room: Exploratory Pilot Study. JMIR Human Factors, 7(3), e19713. doi:10.2196/19713.

Balasubramaniam, V. (2021). Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis. Journal of Artificial Intelligence and Capsule Networks, 3(1), 34–42. doi:10.36548/jaicn.2021.1.003.

Rogers, W. A., Draper, H., & Carter, S. M. (2021). Evaluation of artificial intelligence clinical applications: Detailed case analyses show value of healthcare ethics approach in identifying patient care issues. Bioethics, 35(7), 623–633. doi:10.1111/bioe.12885.

Graber, M. L. (2013). The incidence of diagnostic error in medicine. BMJ Quality & Safety, 22(Suppl 2), 21–27. doi:10.1136/bmjqs-2012-001615.

Tomayko, J. E. (2003). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion (review). Princeton University Press: Technology and Culture, 44(3), 634–635. doi:10.1353/tech.2003.0140.

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., … Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359. doi:10.1038/nature24270.

Chandradevan, R., Aljudi, A. A., Drumheller, B. R., Kunananthaseelan, N., Amgad, M., Gutman, D. A., … Jaye, D. L. (2019). Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Laboratory Investigation, 100(1), 98–109. doi:10.1038/s41374-019-0325-7.


Full Text: PDF

DOI: 10.28991/HEF-2021-02-04-07

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