Today, not only is technology tracking your steps but it is also enabling the far reach of healthcare through telemedicine. Artificial intelligence in healthcare is playing a huge role in these developments. It is helping fine-tune existing technologies to facilitate better delivery of healthcare.

Currently, the value of A.I. in healthcare is evaluated at $10.4 billion, with an expected growth rate of 38.4% between 2022 and 2030¹. This exponential growth is going to enable more personalized healthcare.

How Is A.I. Being Used in Healthcare?

Artificial intelligence in healthcare is incorporating the basics of traditional medicine into a more efficient healthcare delivery system to save time and improve the overall patient care experience.

For starters, most patient data, almost all over the world, is now being recorded digitally. This allows for a more seamless and standardized method to track patient progress. For complex cases, transferring data for specialized care is relatively easy. This reduces geographical limitations when it comes to patient care. 

During the COVID-19 pandemic, it became harder to treat those who lived far away from healthcare facilities. And, even before the pandemic, rural areas were sometimes unable to access lifesaving treatment, due to infrastructural limitations. Telemedicine, particularly the innovations in medical robotics and the A.I. involved in them² is helping bridge this gap.

Machine learning is an aspect of A.I. whereby the A.I. can analyze a large data set and learn from it like a person, but faster and sometimes more accurately. The evolving algorithms of machine learning are helping with the tracking and treatment of patients. A better understanding of health vitals, such as heart rate, changes in breathing patterns, oxygen saturation, and temperature, is encouraging people to become more informed about their own health³, meaning they can begin earlier management of any fluctuations observed.

One application of A.I. machine learning is the efficient scanning of radiological images (e.g. X-rays). Analysis of large data sets can help to note even the smallest of changes that can often be overlooked by the human eye⁴. This allows for early management and treatment, which in cases such as cancer can be lifesaving. 

Early diagnosis can facilitate early treatment with AI-enabled precision. For instance, in cases of cancer, not all drugs can provide equal efficacy for all patients. Some cases with specific gene mutations or particular cell-line origin of cancerous cells can receive targeted treatment designed by A.I. models⁵.

With easy global access to big data, fast-tracking treatment strategies and vaccines was possible during the COVID-19 pandemic⁶. Technology enabled the forecasting of a second and subsequent third waves in various countries as the virus mutated. How the virus would affect a country could also be visualized based on the relevant available data on infrastructure and population density in various countries.

Such unpredictable scenarios also allow for A.I. technological models to evolve and more accurately predict the outcomes in future catastrophes.

One such use is in predicting the outcomes of chronic diseases. While there is standard care given for conditions like diabetes or cardiovascular diseases, various individual patient factors contribute to how the disease progresses. These factors may mean the standard care is futile in slowing down disease progression. A.I. technology can help assess these risk factors to predict outcomes and offer precision medicine⁷, which allows for more tailored treatment strategies for chronic disease, facilitating positive outcomes.

What Are the Advantages of Using Artificial Intelligence in Healthcare?

One of the primary advantages of implementing A.I. systems into healthcare is the automation of processes. By reading large volumes of unstructured data, machine learning algorithms can standardize this data for clinical use by humans.

Using A.I. technology, diagnosis and treatment can be carried out more efficiently. For various health concerns, specific diagnostic tests can be recommended to avoid unnecessary testing, reducing the cost and time for both the patients and the healthcare system. Additionally, the most effective drug components can be used to create a more targeted treatment for disease management⁸.

A.I. applications within healthcare have benefits for other aspects of clinical tasks as well. The systems for administrative tasks and processing insurance claims can become much more efficient using A.I. technology. It can help reduce the time taken in these tasks, which is especially good for patients who become overwhelmed through these procedures; increased efficiency will improve their patient care experience.

Systems using machine learning algorithms have been proven to be more accurate than people in certain situations, for example recognizing emergencies in dispatch calls. A small study noted that machine learning models more effectively tracked emergency calls for possible cardiac arrests versus human dispatchers⁹. A large review article published in Nature found studies supporting that A.I. is as good as or better than people in detecting pathologies in radiology, dermatology, ophthalmology, cardiology, gastroenterology, and even mental health situations¹⁰.

With the increased use of A.I. technology, there will also be more innovation in simpler tasks of healthcare delivery. One such example is teaching efficient communication with patients, especially for those who have critical illnesses¹¹. 

While the costs may seem monumental when investing in such systems, in the long run, A.I. technology aims to reduce these costs both for the healthcare system and patients. With more precision, time saved, and fewer steps involved in the treatment process, A.I. will save a lot on both infrastructural requirements as well as costs for patient care.

Are There Any Drawbacks To Using A.I.?

While the list of benefits of implementing A.I. runs long, there are a few drawbacks it poses, especially during the initial stages.

The primary one is the risk to privacy – data security must be paramount but can be difficult. A security breach in any system can result in easy access to a lot of personal patient data. For the systems to function more efficiently over time, a significant amount of data needs to be collected. If the security systems are not robust enough, there is a high risk of data being leaked and abused¹².

This means that, for the time being, A.I. systems require strict monitoring by humans. However, it is key to avoid any bias, conscious or subconscious, that this consistent human monitoring can bring about. Bias can affect the outcomes of implementing these technologies¹³ and can arise both during the monitoring and tweaking by human overseers and during the development of the A.I. model in the beginning due to unnoticed biases in the data sets used.

Additionally, a lot of these systems are new. For medical professionals who have been practicing for years, the changes in the patient care approach that A.I. models require can be slightly overwhelming. From learning new machines, to training them to a specific task, to finally implementing them within healthcare settings, all these challenges can make older physicians reject newer systems in favor of the familiar.

Finally, A.I. models, for now, are not held to the same ethical standards that practicing medical professionals are. Mistakes made by a system may not go through the same scrutiny as those made by a person. Therefore, when implementing these systems, any such repercussions should be considered and guidelines need to be drawn up for responding to false results or mistakes the A.I. systems make¹⁴.

Conclusion

Technological innovation within the healthcare space is currently at an optimum level. The COVID-19 pandemic revealed several inefficiencies in how healthcare is dispensed. This is why newer intuitive systems are required to bridge these gaps. Artificial intelligence relies on machine algorithms to learn and implement more efficient ways to deliver healthcare. There are many aspects of the healthcare delivery system that the use of A.I. could improve. There are currently a few drawbacks to A.I. systems; however, A.I. technology aims to reduce the disease burden in the long run. With smart devices at almost every fingertip, technology is slowly improving personal healthcare and over time will improve individualized patient care as well.

1. Grand View Research. (n.d.). Artificial Intelligence In Healthcare Market Size Report, 2030. https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-healthcare-market[↩]
2. Denecke, K., & Baudoin, C. R. (2022). A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems. Frontiers in medicine, 9, 795957. https://doi.org/10.3389/fmed.2022.795957[↩]
3. Shameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017). Translational bioinformatics in the era of real-time biomedical, health care, and wellness data streams. Briefings in Bioinformatics, 18(1), 105–124. https://doi.org/10.1093/bib/bbv118[↩]
4. Fakoor, R., Ladhak, F., Nazi, A., & Huber, M. (2013, June). Using deep learning to enhance cancer diagnosis and classification. Proceedings of the international conference on machine learning, 28, 3937–3949[↩]
5. Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021). Artificial Intelligence in Cancer Research and Precision Medicine. Cancer discovery, 11(4), 900–915. https://doi.org/10.1158/2159-8290.CD-21-0090[↩]
6. Haleem, A., Javaid, M., Khan, I. H., & Vaishya, R. (2020). Significant Applications of Big Data in COVID-19 Pandemic. Indian journal of orthopedics, 54(4), 526–528. https://doi.org/10.1007/s43465-020-00129-z[↩]
7. Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K. B., Pitteloud, N., & Chouchane, L. (2020). Precision medicine in the era of artificial intelligence: implications in chronic disease management. Journal of translational medicine, 18(1), 472. https://doi.org/10.1186/s12967-020-02658-5[↩]
8. Anantpadma, M., Lane, T., Zorn, K. M., Lingerfelt, M. A., Clark, A. M., Freundlich, J. S., Davey, R. A., Madrid, P. B., & Ekins, S. (2019). Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads. ACS omega, 4(1), 2353–2361. https://doi.org/10.1021/acsomega.8b02948[↩]
9. Blomberg, S. N., Folke, F., Ersbøll, A. K., Christensen, H. C., Torp-Pedersen, C., Sayre, M. R., Counts, C. R., & Lippert, F. K. (2019). Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation, 138, 322–329. https://doi.org/10.1016/j.resuscitation.2019.01.015[↩]
10. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7[↩]
11. Butow, P., & Hoque, E. (2020). Using artificial intelligence to analyse and teach communication in healthcare. Breast (Edinburgh, Scotland), 50, 49–55. https://doi.org/10.1016/j.breast.2020.01.008[↩]
12. Murdoch B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC medical ethics, 22(1), 122. https://doi.org/10.1186/s12910-021-00687-3[↩]
13. Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns (New York, N.Y.), 2(10), 100347. https://doi.org/10.1016/j.patter.2021.100347[↩]
14. Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, 295–336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5[↩]

Dr. Michelle Frank

Michelle is a healthcare consultant and content creator with over six years of experience in the FemTech space. She contributes extensively to health forums, especially those centered on enabling wellness, advancing digital health, and FemTech solutions. She loves classic rock music, reading classic literature, and finding new spots in town for good food and some chai.