Artificial intelligence: Current and future impact on general practice

Abstract

We live in an age where a vast amount of information is being generated daily and this, combined with advances in computational power, provides fertile ground for numerous applications of artificial intelligence (AI). In medicine, we continue to see ground-breaking applications of AI, for example, in the detection of abnormalities on X-rays, skin lesion diagnostics, retinal image diagnostics and symptom triage. These applications have raised questions about the safety, ethics and limitations of AI in clinical diagnosis and patient care. There is no doubt that AI has much to offer clinicians in primary and secondary care. This article explores current and future applications of AI in primary care.

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References

ASI Data Science (2018) Summarise and explore pandas dataframes, https://pypi.org/project/lens (accessed May 2021).

Comtois D (2019) Tools to quickly and neatly summarise data. Available at: https://CRAN.R-project.org/package=summarytools (accessed May 2021).

Econsult Health Limited (2013) e-consult. Available at: https://econsult.net/about-us (accessed May 2021).

Hippisley-Cox J, Coupland C and Brindle P (2017) Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: Prospective cohort study. British Medical Journal 357: j2099. DOI: 10.1136/bmj.j2099.

Kueper

Terry

Zwaenstein

, et al.(2020) Artificial intelligence and primary care research: A scoping review. Annals of Family Medicine 18(3): 250–258. DOI: 10.1370/afm.2518.

McCarthy J (2007) What is artificial intelligence. Available at: http://jmc.stanford.edu/articles/whatisai.html (accessed 1 May 2021).

McCarthy

Minsky

Rochester

, et al.(2006) A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine 27(4): 12–14. DOI: 10.1609/aimag.v27i4.1904.

Mills M (2016) Artificial intelligence in law: The state of play 2016 (part 1). Available at: www.legalexecutiveinstitute.com/artificial-intelligence-in-law-the-state-of-play-2016-part-1/ (accessed 1 May 2021).

The Phoenix Partnership (2008). SystmOne. Available at: www.tpp-uk.com/products/systmone (accessed 01 May 2021).

10.

Weng

Reps

Kai

, et al.(2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One 12(4): e0174944DOI: 10.1371/journal.pone.0174944.

11.

Wiesner

Pfeifer

(2014) Health recommender systems: Concepts, requirements, technical basics and challenges. International Journal of Environmental Research and Public Health 11(3): 2580–2607. DOI: 10.3390/ijerph110302580.

12.

Zhu G, Jiang B, Tong L, et al. (2019) Applications of deep learning to neuro-imaging techniques. Frontiers in Neurology 10: 869. DOI: 10.3389/fneur.2019.00869.