McMaster HealthTech

Alexa Braganza — McMaster University, Honours Life Sciences 2027

Artificial Intelligence, or AI, is revolutionizing our modern approach to health care and patient treatment. As published in The New England Journal of Medicine, Dr. Yuki Kataoka and Dr. So Ryuhei highlighted that the most prominent concern with AI medical diagnoses is that AI cannot think critically as humans. Researchers have claimed that because of this, AI may not provide consistent, accurate results and thus, medical professionals will still need to rely on their education to diagnose.¹ Dr. Daniele Giansanti, a specialist in microelectronic engineering, discussed that although AI can help accelerate the interpretation of data and the results of medical tests, it is crucial that AI does not replace human research laboratories and practices.² 

AI lacks the training, critical thinking and empathy of medical professionals, and thus should be used to assist them rather than being seen as a replacement. In addition to AI’s possible ethical issues in medical practices, the possible advantages of AI in healthcare have been a popular topic of discussion within the scientific community in recent times. Kataoka and Ryuhei (2023) claimed that AI is a resourceful accumulation of knowledge that can support decision-making and future research analysis. This is a powerful tool for future researchers to design experiments that AI can interpret since it would establish faster results and facilitate the advancements of medications and treatments. For example, radiologists have been programming AI to read and diagnose images accurately.⁴ 

Similarly, AI has been proven to process data accurately, specifically with tests and research results that are difficult to process due to the large data sets.² Kumar et al. (2022) claimed that the efficiency in tracking data using AI over the long term is more efficient when extrapolating results than other methods of analyses.³ Thus, AI can help speed up the process of possible new treatments being approved due to AI’s ability to accelerate research and analyze existing evidence. However, now that AI has been implemented into medical practices, there are concerns regarding patient comfort when communicating with AI. 

In addition, researchers have confirmed that a diagnosis generated by AI may seem unreliable and inconclusive to a patient compared to a diagnosis by a doctor.⁴ As Kataoka and Ryuhei (2023) suggested, medical students must be trained by incorporating AI into their studies and hospital placements. Over time, patients will then be more comfortable with AI assisting doctors in simple practices. Many scientists agree that to increase patient comfort, there must be a clear justification of what data was extrapolated from AI to reach a medical diagnosis.⁴ 

While AI is becoming more prominent in other areas of science, researchers have been keen to find the implications and limitations of AI in medicine. As Kumar et al. (2022) suggested, AI should be considered to enhance the medical profession due to its efficiency in high-risk decision-making, which helps inform doctors of the most logical decision for a better outcome for the patient.³ As Giansanti (2023) mentioned, “AI will transform health care” and is set to revolutionize future medical practices.² Therefore, as AI becomes more prominent in medicine, professionals must follow ethical guidelines that balance between trusting AI and consulting medical professionals.

Works Cited

1. Kataoka, Y. & Ryuhei, S. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. The New England journal of medicine. [Internet] 2023June22 [cited 2023 Dec3]; 388 (25), 2399–2400. Available from: doi.org/10.1056/NEJMc2305286.
2. Giansanti, D. Precision Medicine 2.0: How Digital Health and AI Are Changing the Game. Journal of personalized medicine. [Internet] 2023June28 [cited 2023Dec3]; 13 (7), 1057 Available from: doi.org/10.3390/jpm13071057.
3. Kumar, Y., Koul A., Singla R., Fazal Ijaz M. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. National Library of Medicine. [Internet] 2022Jan13 [cited 2023Dec3]; 14(7): 8459–8486. Available From: doi.org/10.1007/s12652-021-03612-z.
4. Zhang, P. & Kamel Boulos, M. N. Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges. Future internet. [Internet] 2023Aug24 [cited 2023Dec3]; 15 (9), 286–287. Available from: https://www.mdpi.com/1999-5903/15/9/286.