The Rise of AI in Healthcare
It had been a while since a medical student in his early twenties pondered a question: What lay ahead for his healthcare career aspirations, built through various experiences? He couldn’t pinpoint when the uncertainty about his future as a medical professional came to be a central thought in his morning coffee run and late-night dinners. This path was meant to be his way of giving back to the community. But there was one thing he was certain about: AI had arrived. The doctors he shadowed were already using it in their diagnostic tools. He could easily picture a future where AI shook up regular medical practices, becoming a key part of healthcare.
Based on a study about medical students’ perceptions about radiology and other specialties in relation to AI, various medical students across medical schools in the US share the same sentiments.
— Of 156 students, over 75% agreed that AI would play a significant role in the future of medicine [1a].
— 44% reported that AI made them less enthusiastic about radiology, a specialty labelled as the most likely to be affected and/or replaced by AI by 66% of the student’s surveyed [1a].
In another study, it was observed that medical students exhibit greater concerns compared to radiology trainees and radiologists about the impact of AI on their decision to choose radiology as a medical specialty. This heightened concern was thought to be attributed to their limited exposure to the field of radiology and knowledge about AI [2a].
Nonetheless, these collective experiences shed light on the evolving landscape of medical education and the pivotal role that AI is positioned to play in the future of oncoming medical professionals and the medical field as we know it.
As we explore the transformative impact of AI on diagnostics, symptom-checking, and overall healthcare efficiency, it’s crucial to understand that these advancements are not hypothetical concepts but are grounded in extensive research and real-world applications.
Diagnostic Tools: Unlocking Precision Medicine
AI-powered diagnostic tools are proving to be invaluable in primary care settings. A paper on the role of AI in revolutionizing health especially dives into the exciting possibilities of AI in healthcare diagnostics. Imagine a future where AI transforms how we diagnose diseases, choose treatments, and conduct clinical tests. Based on the authors, the key lies in AI’s ability to handle massive datasets and spotting intricate patterns, outperforming humans in various healthcare tasks [3a].
Indeed, several compelling studies highlight the transformative impact of AI in various medical diagnoses and healthcare tasks:
1. In the UK, a study utilizing AI for breast cancer diagnosis from mammograms demonstrated a remarkable absolute reduction in false positives and false negatives by 5.7% and 9.4%, respectively [4a].
2. In South Korea, AI surpassed radiologists in sensitivity, diagnosing breast cancer with masses at 90% compared to 78% and excelled in detecting early breast cancer at 91% versus radiologists’ 74% [5a].
3. Moreover, in skin cancer detection, an AI using CNN outperformed dermatologists in accurately diagnosing melanoma cases and recommending treatment options [6a, 7a].
What makes this so impactful? AI has the potential to revolutionize healthcare by significantly improving diagnostic accuracy, cutting costs, and saving time compared to traditional methods. It’s not just about technology; it’s about reshaping how we approach healthcare, promising a future where precision and efficiency take center stage.
Remote Consultations and Telemedicine:
A report from the World Health Organization (WHO) acknowledges the role of AI in facilitating remote consultations which would offer a viable solution to address healthcare disparities. As stated in the report, AI could “enable resource-poor countries and rural communities, where patients often have restricted access to health-care workers or medical professionals, to bridge gaps in access to health services” [11a].
Symptom-Checkers: From Information to Empowerment
Other initiatives in integrating AI in healthcare have not only reaffirmed the potential of AI-driven symptom checkers but have also underscored their pivotal role in transforming healthcare decision-making.
An MDPI article, “Redesigning Primary Care: The Emergence of Artificial-Intelligence-Driven Symptom Diagnostic Tools,” sets the stage by proposing that AI-based symptom checkers can optimize medical history-taking for both practitioners and patients, revolutionizing primary care and enhancing diagnostic accuracy and efficiency.
The insights from the Medical Device Network article further amplify how AI has the power to revolutionize healthcare. These tools not only guide patients to timely and appropriate care but also play a crucial role in reducing unnecessary health visits. Indeed, a main sentiment was that symptom checkers may “play a crucial role in the triage process”, ensuring optimal resource allocation and timely medical intervention [8a].
All in all, empowering individuals to make informed healthcare decisions, AI symptom checkers can provide impressions of potential health issues, reinforcing the shift towards patient-centric and proactive healthcare.
The Efficiency Revolution
Most doctors will agree that much of today’s ‘doctoring’ is spent on paperwork and insurance billing rather than actual interactions with patients.
One of the primary advantages of incorporating AI into primary healthcare lies in the enhancement of efficiency across various aspects of the healthcare system. With an automated system, it is not difficult to grasp how AI can potentially reduce the time doctors and other medical professionals devote to menial tasks and paperwork, thereby allowing for more time dedicated to the patients themselves.
Indeed, in an interview of Emil Lárus Sigurðsson, MD, PhD, a PCP and professor in the department of family medicine at the University of Iceland and Steindór Ellertsson, MD, using AI was conveyed as a way in which they were exploring how to increase the quality of patient care through delegating tasks such as initial triaging of patients to AI [9a].
Combating the elephant in the room, or the record keeping and insurance billing routine of doctors in between patient visits and toward the end of the workday, AI has the potential to reduce the administrative burden on healthcare professionals, allowing them to focus more on direct patient care.
The Dangers and Limitations of AI
In embracing the promising landscape of AI-driven healthcare, it is crucial to acknowledge and address the limitations and potential dangers inherent in the integration of artificial intelligence as a diagnostic and treatment tool. The insights gained from the provided sources shed light on several key concerns that demand thoughtful consideration.
1. Injuries and Errors:
AI systems, while capable, are not infallible. Errors in healthcare recommendations, such as recommending the wrong drug or overlooking critical medical information, pose a tangible risk to patient safety. These mistakes underscore the need for rigorous oversight and continuous refinement of AI algorithms to minimize the potential for patient harm.
The potential of AI to yield incorrect information was especially evident in an BMJ open articles which scrutinized the accuracy of 8 popular digital symptom assessment apps providing urgency advice [10a]. The general results were as follows: some apps are good at giving suggestions for many users, while others not so much. When they compared them to doctors, the top three apps got it right about 82% of the time. But here’s the thing — some apps did better when they left out certain users or conditions. Doctors, on the other hand, were super reliable, giving safe advice about 97% of the time. However, not all apps were as consistent. Some were good, staying close to what the doctors advised, while others were a bit off.
This whole review makes it clear: these apps still have some room to improve, especially when it comes to accuracy and safety. So, if you’re using one, keep in mind they’re not all the same, and it’s good to be cautious and maybe double-check with a real-life doctor for important decisions about your health.
2. Privacy Concerns
The incorporation of AI systems in healthcare introduces privacy challenges, especially in data collection and usage. The need for enormous amount of data for training models may incentivize developers of algorithms to collect these data from patients. Hence, proper guidelines for collecting and using patients’ data for training models must be established to ascertain the protection of patients’ privacy.
To ensure positive global health impact and protect patient privacy, the World Health Organization (WHO) outlines six key principles [11a]. The foremost principle emphasizes human autonomy, asserting that “humans should remain in control of health-care systems and decisions,” stressing explicit measures for “privacy and confidentiality.” It highlights the importance of securing patient information and the need for patients’ valid informed consent.
Overall, the AI race is on, but we need to ensure that firm foundations are established with the best interests of patients in mind. Needless to say, the needs of the patients should always come first before any discussions of increasing efficiency or lessening economic burdens.
Footnotes:
1a. Park, C. J., Yi, P. H., & Siegel, E. L. (2021). Medical Student Perspectives on the Impact of Artificial Intelligence on the Practice of Medicine. Current problems in diagnostic radiology, 50(5), 614–619. https://doi.org/10.1067/j.cpradiol.2020.06.011
2a. Radiology as a Specialty in the Era of Artificial Intelligence: A Systematic Review and Meta-analysis on Medical Students, Radiology Trainees, and Radiologists.
3a. Always, S.A., Alghamdi, S.S., Alsuhebany, N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 23, 689 (2023). https://doi.org/10.1186/s12909-023-04698-z.
4a. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94. https://doi.org/10.1038/s41586-019-1799-6.
5a. Kim H-E, Kim HH, Han B-K, Kim KH, Han K, Nam H, et al. Changes in cancer detection and false-positive recall in mammography using Artificial Intelligence: a retrospective, Multireader Study. Lancet Digit Health. 2020;2(3). https://doi.org/10.1016/s2589-7500(20)30003-0.
6a. Han SS, Park I, Eun Chang S, Lim W, Kim MS, Park GH, et al. Augmented Intelligence Dermatology: deep neural networks Empower Medical Professionals in diagnosing skin Cancer and Predicting Treatment Options for 134 skin Disorders. J Invest Dermatol. 2020;140(9):1753–61. https://doi.org/10.1016/j.jid.2020.01.019.
7a. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8):1836–42. https://doi.org/10.1093/annonc/mdy166.
8a. Wiedermann, Christian J., Angelika Mahlknecht, Giuliano Piccoliori, and Adolf Engl. 2023. “Redesigning Primary Care: The Emergence of Artificial-Intelligence-Driven Symptom Diagnostic Tools” Journal of Personalized Medicine 13, no. 9: 1379. https://doi.org/10.3390/jpm13091379
9a. Q&A: AI can triage patients in primary care, streamline health care services (healio.com)
10a. Gilbert, S., Mehl, A., Baluch, A., Cawley, C., Challiner, J., Fraser, H., Millen, E., Montazeri, M., Multmeier, J., Pick, F., Richter, C., Türk, E., Upadhyay, S., Virani, V., Vona, N., Wicks, P., & Novorol, C. (2020). How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs. BMJ open, 10(12), e040269. https://doi.org/10.1136/bmjopen-2020-040269
Welcome to WordPress! This is a sample post. Edit or delete it to take the first step in your blogging journey. To add more content here, click the small plus icon at the top left corner. There, you will find an existing selection of WordPress blocks and patterns, something to suit your every need for content creation. And don’t forget to check out the List View: click the icon a few spots to the right of the plus icon and you’ll get a tidy, easy-to-view list of the blocks and patterns in your post.
Desir Research Group 