Written by Taylor Le and Edited by Kevin Liu
Artificial intelligence (AI) has been a traditionally used computing term that involves simulating human thought and intelligence in machines. Computing power, when combined with machine learning, which are algorithms that perform data analyses that improve with iterations) has been improving rapidly over the past several decades in various fields, namely computer science and software engineering [1]. Additionally, recent developments specifically in the healthcare field may pave the way for a multidisciplinary team by providing medical professionals with AI-aided computing for a refined database of healthcare prevention, treatment, and diagnosis. Rather than associating medicine and the direction of healthcare with solely physicians, nurses, and other crucial members, people are increasingly learning of the emergence of AI and its drastic implications and advantages in a healthcare setting.
Traditionally, the backbone of a career in healthcare focuses on disease diagnosis, treatment, and prevention. While specific programs and computational processing have always aided the healthcare system, the widespread implementation of AI to create a multidisciplinary approach is the next major step in advancing medical treatment. AI-aided computing can efficiently sift through, analyze, and generate a multitude of readings, risk assessments, treatment probabilities, and statistics based on the patient’s medical records and millions of others with similar symptoms and conditions [2].
While AI and machine learning are facilitated through experience and human intervention to make necessary corrections and adjustments, the combined efforts thus far have paved the way for groundbreaking developments, namely early detection of cancer, respiratory failure, and strokes. Recent research demonstrates the capability of AI and machine learning in early stroke detection by monitoring physiological activity to detect when irregular patterns arise and persist. Once detected, it sends an alert to the healthcare team to quickly take action to treat the patient [3]. Early detection is also available for breast and colorectal cancer patients, and its implications in estimating one’s survival rates is also being analyzed [4]. Additionally, the Medical Center of the Albert Einstein College of Medicine has implemented an AI technology known as PALM (Patient-Centered Analytic Learning Machine), which is able to analyze patterns and physiological conditions to detect and assess respiratory failure in patients. Some benefits include faster patient discharge and improved overall patient care [2].
Although the diagnosis and treatment of diseases that fully combines the experience of healthcare professionals and computing prowess of machines have yet to be implemented, AI has become increasingly advanced and beneficial in specific cases. As of now, however, fully implementing AI is difficult due to its expensive startup costs in manufacturing, implementation, training, and need for constant human intervention. While technology and AI will never be perfect, the advantages and possibilities open to the healthcare field are the future pillars to a holistic and comprehensive definition of healthcare and medicine. Outside the realm of data analysis, AI may make its way into directly assisting during procedures and operations, performing on-the-fly tests and analyses in the case of emergencies, and much more. Research into AI is continuously developing and requires further clinical testing before being implemented completely. Yet, current uses of AI have been promising, and such benefits may dramatically improve aspects of treatment and recovery for patients in the near future [4].
References
- Batra, R., Song, L., Ramprasad, R. (2020). Emerging materials intelligence ecosystems propelled by machine learning. Nature Reviews Materials.
- Freethink. Saving Lives with AI | Freethink. 2018. YouTube, www.youtube.com/watch?v=VePHPymCy2U&t=145s. Accessed 12 Nov. 2020
- Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2:230–243.
- Ramesh, A.N., Kambhampati, C., Monson, J.R.T., Drew, P.J. (2004). Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England, 86:334–338.