Artificial Intelligence in the Healthcare and Medical Field
Home & Introduction
Benefits
One of the benefits of AI in the health and medical field - and almost all industries that implement AI for that matter - is that it can significantly lighten the workload. AI is great at pattern recognition, data analysis, detection, and automation. All these skills make it a useful resource to quickly analyze copious amounts of data that would take humans a long time and lots of effort. This is extremely beneficial for dealing with substantial amounts of low-risk data, and repetitive tasks. In the medical field specifically, it can help reduce physician burnout, allowing our healthcare professionals to spend more time on other matters only they can tend to. Burnout should be taken seriously, it is an emotional and mental exhaustion that also has physical manifestations that can have serious consequences. Some consequences can be worsening mental and physical health, decreased performance, along with a few less severe symptoms like irritability. This can result in decreased productivity and may increase the likelihood of human error (Fawzy).
The combined skills that AI has to offer like analysis, recognition, and detection, can not only help revise data and automate conclusions and diagnosis, but paired with other skills like prediction, forecasting, and personalization make it perfect for personalized health care. As we know AI is very efficient, completing things much faster, and even more accurately than we can, which makes it perfect for creating personalized treatment and care plans for each individual patient based on their history, records, and results. This allows us to save time, in researching, testing, and trying to decide which medicines, treatments, and approaches would work best.
Current Implementations
There are some current uses for AI in the medical and healthcare field. It is important to note that all of its intended purposes are used as supplementary tools in conjunction with professional knowledge. It is not completely in control of any applied use in the industry. It is mainly used to aid and assist. This technology is also used to lighten the workload by taking care of daily administrative functions like categorization, prioritization, organization, and answering basic questions as well as dealing with billing and insurance information.
- Daily Administrative Functions
- Aid in Diagnosis and Early Detection of diseases
- Assist in Personalised Healthcare plans
- Research
- Prediction Models (disease progression, reaction to medication and treatment)
- Education
- Help with Remote Monitoring
Due to medical advancements in recent decades, we are witnessing the largest and still growing aging population in history. This has pushed countries to invest more in medical infrastructure and has increased the demand for personalized medicine. Personalized medicine or care recognizes that each patient has unique needs, influencing factors, and defining characteristics that shape their treatment and healthcare approach. AI can help in this aspect by taking patient data, records, and feedback and turning it into viable solutions or changes that can be directly implemented to help with patient satisfaction. Medical staff is already so busy, and often overworked, AI can help alleviate some of the demands placed on healthcare workers.
AI can be very helpful when it comes to research. AI is able to create prediction models for a variety of uses like disease prediction, and predictions of reactions that may result from a medication (Med School Insider). This has saved significant time and resources. Previously, when patients tried new medications, they would be prescribed them and have to wait weeks or months to determine their effectiveness. AI creates predictions about what the patients' reactions may be based on patient records, history, and known information about the medication. This can save patients and doctors time, unnecessary setbacks, and even suffering.
With these predictions, AI is also suitable to create disease progression predictions, based on individual factors like demographics, disease, progression, reaction to treatment, and risk factors (Nancy). Taking into account so many influencing, and patient-specific factors, these disease progression predictions can be remarkably precise. This can help doctors determine what the best course of treatment could be based on these predictions and contributing factors. As I stated before, this can help save time, which may be a critical factor for some patients, and could be the difference between life or death.
Another implementation of AI is in medical education, across all levels. This could be AI studying tools, and even practice tools for students, like AI patients. These AI patients allow for hands-on practice and experience; this allows for mistakes to be made with no real consequence, to a living patient. This low-risk environment makes practice more accessible, and since experience in the field is the key to learning, it provides students with more opportunities for hands-on engagement.
Another method in which AI is applied is for remote monitoring. This advancement has allowed patients to go home when they are no longer high-risk, but still require around-the-clock monitoring (American Medical Association). This allows low-risk patients to decrease their hospital stays, while still receiving the supervision they need. This facilitates the optimal use of facilities, meaning more rooms will be available for patients who are in more urgent need, and also shorten hospital stays.
Challenges & Limitations
- Data security
- Incomplete databases
- Biased algorithms
Like with many things over the internet, security is a main concern. With the recent pandemic we have seen a surge in telemedicine usage; likely due to the overcrowding in hospitals. The concern here is the data security risk, the implementation of AI means allowing access to a lot of personal and sensitive information being communicated across these platforms. Although most are encrypted, the security measures aren’t as sophisticated as the ones used in Health or Electronic Information systems. This can make them susceptible to cyberattacks and allows for a greater risk of the misuse of this sensitive information (Tiribelli).
Another issue with using AI is algorithm limitations, and the need for comprehensive datasets (Basu). In order to accurately generate and carry out these tasks, they do it through algorithm, and data retrieval to generate results. The problem is that often databases are limited and incomplete; it is nearly impossible to gather every medical finding, research, journal, or article. Though findings are not usually inaccurate they aren’t guaranteed to be 100% correct, or up to date with the newest medical findings. For AI to maintain accurate and unbiased automation, it is essential that the data it draws from is as widespread, comprehensive, and precise as possible.
The Future of AI in Medicine & Healthcare
AI-driven technology and implementation are no longer a side project. Millions are being poured into generating and refining this technology, which is designed for the specific needs of the health and medical field (Basu). The implementation of AI up until now has been minimal, but with so much money, research, and innovation going into these products, I think in the next few decades, we will see an increase in implementation on a wide scale, though I don’t think they'll be taking our jobs anytime soon.
In Conclusion
References with Paraphrasing
American Medical Association (AMA). (2024, December 13). Health care technology trends 2025: AI benefits, wearable use cases, and telehealth expansion. YouTube. https://www.youtube.com/watch?v=8OWdxCJcQVE
This Interview addresses many topics, and talks about predicted trends regarding AI in the medical field. Increased implementation and how we deploy the use of these AI technologies. Some current uses like Remote Patient Monitoring, facilitate around-the-clock monitoring and promote patient engagement. Thoughtful integration, and designing technology based on physician and clinician needs. The slight decline in Telehealth and responsible usage of these sites. It can relieve some of the Cognitive Burden by creating a network of synthesized clinical data.
Basu, K., Sinha, R., Ong, A., & Basu, T. (2020). Artificial Intelligence: How is It Changing Medical Sciences and Its Future?. Indian journal of dermatology, 65(5), 365–370. https://doi.org/10.4103/ijd.IJD_421_20
This article defines AI and establishes the history of the use of AI in the medical field; giving examples of past and current implementation, as well as companies that use this technology. Addressing limitations, and common misinterpretations about AI. Overall this article gives refreshing perspectives and realistic aspects regarding, the current, actual, and future roles of AI in the medical Field.
Fawzy, E. M. (2024). Using Artificial Intelligence in Electronic Health Record Systems to Mitigate Physician Burnout: A Roadmap. Journal of Healthcare Management, 69(4), 244-254. https://doi.org/10.1097/JHM-D-24-00094
This article talks about physician burnout, and how the implementation of AI could be beneficial, for physician well-being, as well as efficacy. The uses of AI talked about in the article are mainly for administrative tasks, routine responsibilities, or “busy work” that can alleviate the workload for the physician and help combat possible human error; by revising work and also indirectly by reducing burnout, therefore less room for error due to exhaustion. The article talks about the integration of AI into Electronic Health and how we can implement AI in the form of predictions based on algorithms and simulations, categorization, prioritization, and organization.
Nancy, S. G., & Kumar, P. (2023). Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. Computers in Biology and Medicine, 162https://doi.org/10.1016/j.compbiomed.2023.107051 by artificial intelligence in big data management and analysis to hinder precision medicine.
This article talks about the integration and implementation of AI in the Medical field and also addresses common concerns about data management and accuracy. AI has successfully been implemented in many forms like AI-assisted diagnosis and early detection of diseases. There are some limitations such as proper security and encryption and the cost that comes with it, and incomplete databases; because of this integration of AI in the healthcare field has been slower than in other industries. The purpose of using AI and machine learning (ML) is to eventually mimic the cognitive abilities of the human brain, but as we know that is very complex, so implementations are not used exclusively, but as a hybrid application, used more as an assistance or tool in coordination with physician knowledge.
Med School Insiders. (2023). 4 Ways Artificial Intelligence is Transforming Healthcare. In YouTube. https://www.youtube.com/watch?v=TfkHrvct1hg
This video talks about the current uses of AI in the health and medical field, using specific examples. Using AI as an aid in diagnosing, like CNN; is used as a supportive tool alongside physicians. Prediction models that may help determine disease progression or responses to medications and treatments. AI also allows for personalized medicine, synthesizing knowledge and clinical studies, with the patient's unique needs, symptoms, and characteristics. The use of AI for administrative tasks, like scheduling, or answering basic general questions. Also the use of AI in Medical Education and training on all levels.
Tiribelli, S., PhD., Monnot, A., M.Sc, Shah, Syed F H, BA,M.B., B.Chir, Arora, Anmol,M.B., B.Chir, Toong, P. J., & Kong, S., M.Phil. (2023). Ethics Principles for Artificial Intelligence–Based Telemedicine for Public Health. American Journal of Public Health, 113(5), 577-584. https://doi.org/10.2105/AJPH.2023.307225
AI-powered telemedicine can support and aid healthcare systems but comes with ethical risks. Telemedicine has grown exponentially in the past decade, especially during the pandemic, but there is still a large gap in ethical guidelines regarding AI of telemedicine in many aspects, such as accuracy, and confidentiality; this lack of “rules” is a critical aspect that brings concerns about the proper usage, or possible misuse of these platforms. The application of AI and ML have proved helpful in remote monitoring, predictions, diagnostic support, robot-assisted treatment, and research, but still, the lack of context-sensitive information and the vulnerability of sensitive information pose a big threat to its implementation.
Comments
Post a Comment