Aditya Bhardwaj – McMaster Life Sciences 2025
A key issue in the healthcare systems of both the USA and Canada alike has been the lack of healthcare services available to those who live in rural or underdeveloped areas.1,2 People who live in these areas are burdened by many consequences of this, such as more difficulty accessing treatments and medical screenings3, which would translate to a higher risk of death from diseases, such as cancer.4 While solutions like telemedicine have improved the quality of healthcare in rural areas, they have drawbacks, namely a reduction in the quality of diagnosis, as it can be harder to diagnose virtually as opposed to in person.5
With the sudden rise of AI in the past few years, many have considered its place in medicine, and how it can contribute to the field. As some examples, AI has already been applied to cancer screenings, controlling the dosage of anesthesia given to a patient, and predicting the likelihood that in-vitro fertilization, commonly known as ‘test tube babies’, will be successful.6,7,8 As such, it is not surprising that AI is also being explored as a solution to the lack of healthcare available in rural settings.
One immediate impact AI can have on rural clinics is by allowing for prompt analysis of any imaging, such as X-rays. Rural clinics often do not have the staff nor the equipment to analyze images in a timely manner. With new AI technologies now being able to analyze medical images9, rural clinics could rapidly improve the quality of their diagnoses and provide prompt treatment, rather than having to rely on the help of urban areas. Further, people who live in rural areas are at a higher risk of mental health disorders, as well as an increased risk of suicide.10 To make matters worse, availability to mental health programs has actually decreased over time in rural areas, which could further exacerbate this issue.11 Using its predictive capabilities, AI can potentially identify high-risk individuals or populations in rural areas, which would allow doctors to intervene appropriately. A 2018 study used an AI processor and found that it could predict the onset of psychosis, a mental disconnection from reality, with 72% accuracy, highlighting its potential applications in the future.12
Figure 1. Applications of AI in advancing medical knowledge (a) and in patient care (b). SOURCE: Ground Truths
To further emphasize an issue in the USA, the country has one of the highest maternal mortality rates when compared to other developed countries.13 This would be a greater issue in rural areas as well, given the lack of access to proper care. AI has the potential to, not only monitor the condition of the mother and baby and predict their survival, but also allow urban hospitals to view the mother and child at the same time, preventing pregnant mothers from making long trips.14
However, there are still challenges to overcome before AI can become a full-fledged solution for this. The concerns are not necessarily about AI in rural areas, but more so general issues with AI usage in medicine. One hurdle would be the fact that it is a significant task to teach AI usage to all healthcare professionals and to get them accustomed to it.15 With 2 out of 5 physicians expected to be 65 years or older in the USA within the next decade, coupled with the costs and time needed for this to happen, it is clear that this would take a massive effort.16 Additionally, there are concerns about cybersecurity and the ability of AI to safeguard patient information.17 While AI is still in the early stages of its development, making these obstacles expected as a part of any new innovation, the potential for expansion and application is massive, rendering it a potent solution in the race to fulfill rural healthcare needs.
Works Cited
1. James CV, Moonesinghe R, Wilson-Frederick SM, Hall JE, Penman-Aguilar A, Bouye K. Racial/Ethnic Health Disparities Among Rural Adults — United States, 2012–2015. MMWR Surveillance Summaries. 2017;66(23):1-9.
2. Lavergne MR, Kephart G. Examining variations in health within rural Canada. Rural Remote Health. 2012;12:1848.
3. Onega T, Hubbard R, Hill D, Lee CI, Haas JS, Carlos HA, et al. Geographic access to breast imaging for US women. J Am Coll Radiol. 2014;11(9):874-82.
4. Henley SJ, Anderson RN, Thomas CC, Massetti GM, Peaker B, Richardson LC. Invasive Cancer Incidence, 2004-2013, and Deaths, 2006-2015, in Nonmetropolitan and Metropolitan Counties – United States. MMWR Surveill Summ. 2017;66(14):1-13.
5. Hwei LRY, Octavius GS. Potential advantages and disadvantages of telemedicine: A literature review from the perspectives of patients, medical personnel, and hospitals. Journal of Community Empowerment for Health. 2021;4(3).
6. Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Communications. 2021;41(11):1100-15.
7. Bellini V, Valente M, Gaddi AV, Pelosi P, Bignami E. Artificial intelligence and telemedicine in anesthesia: potential and problems. Minerva Anestesiol. 2022;88(9):729-34.
8. Emin EI, Emin E, Papalois A, Willmott F, Clarke S, Sideris M. Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward? In Vivo. 2019;33(5):1547-51.
9. Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954-61.
10. Morales DA, Barksdale CL, Beckel-Mitchener AC. A call to action to address rural mental health disparities. J Clin Transl Sci. 2020;4(5):463-7.
11. Henke RM, Fingar KR, Jiang HJ, Liang L, Gibson TB. Access To Obstetric, Behavioral Health, And Surgical Inpatient Services After Hospital Mergers In Rural Areas. Health Aff (Millwood). 2021;40(10):1627-36.
12. Corcoran CM, Carrillo F, Fernández-Slezak D, Bedi G, Klim C, Javitt DC, et al. Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry. 2018;17(1):67-75.
13. Alkema L, Chou D, Hogan D, Zhang S, Moller A-B, Gemmill A, et al. Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Maternal Mortality Estimation Inter-Agency Group. The Lancet. 2016;387(10017):462-74.
14. Siwicki B. AI-powered RPM can help address the rural neonatal care crisis 2022 [Available from: https://www.healthcareitnews.com/news/ai-powered-rpm-can-help-address-rural-neonatal-care-crisis.
15. Petersson L, Larsson I, Nygren JM, Nilsen P, Neher M, Reed JE, et al. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research. 2022;22(1).
16. Boyle P. Aging patients and doctors drive nation’s physician shortage: AAMC; 2022 [Available from: https://www.aamc.org/news/aging-patients-and-doctors-drive-nation-s-physician-shortage.
17. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine. 2019;17(1).
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