How Artificial Intelligence Will Improve Global Healthcare and Democratize Services

How Artificial Intelligence Will Improve Global Healthcare and Democratize Services

Authored by: Rae Steinbach
Edited by: Gaurav Dubeyartificial intelligence medicine future

Breakthroughs in technology and medicine often go hand-in-hand. New tests are devised, new drugs developed, and new diagnostic tools are created. As we move headlong into the digital future, the medical field is ripe for exploration, with expected improvements in the diagnosis and treatment for a variety of ailments.

 

Artificial intelligence (AI) developments work to create computer-generated applications that are so sophisticated that interacting with them will be virtually indistinguishable from humans. To accomplish this goal, developers are designing programs that mimic human brain function, specifically the concept of neural networks through a process called biomimicry (Bini, 2018).

 

With each passing year, the medical field becomes more complicated than ever. Physicians and caregivers are swamped with administrative work, keeping up with the latest literature, as well as researching and applying new ideas. Of course, they then need to carve out time for their actual patients too. AI programming, among many goals, strives to take the brunt of routine labor out of the equation for clinicians, while analyzing data faster and with a higher level of accuracy.

AI Diagnostic Tools are the Wave of the Future

Artificial intelligence, in particular, is gaining traction as a diagnostic tool. For many, linking AI  and medicine sounds like the stuff of science fiction. The late author Isaac Asimov, however, noted long ago that “today’s science fiction is tomorrow’s science fact.”artificial intelligence isaac asimov

 

Pattern recognition may be one of the biggest diagnostic breakthroughs in AI technology. By ‘training’ programs to recognize specific types of cells, it can potentially be useful in analyzing tissue samples to accurately and efficaciously identify and diagnose diseases.

 

Researchers in Japan, for instance, have developed an AI program that can examine colorectal polyps for cancer in real-time (Misawa et al., 2018). Abnormal tissue growth has been assessed with 79% positive and 93% negative successful predictive rates. They hope their recent results are promising enough to go forward with clinical trials soon.

 

Similar research into breast cancer diagnosis has been demonstrated through AI algorithm competitions (Ehtashami et al., 2017). The best of the algorithms are able to detect lymph node metastases comparable to human pathologists and, in some cases, outperform their accuracy. The goal here is not to replace the pathologist, but to assist them with more precise and timely diagnosis.

 

The increased accuracy and efficiency that AI is poised to bring to the medical field may indeed prove to be an invaluable resource in the near future. One of the most notable improvements to AI-integrated healthcare is expected to be the rapidity with which a patient will be diagnosed. A recent study by Zhang and colleagues demonstrated the remarkable efficacy in the rapid and accurate “intraoperative pathological diagnosis” (IOPD) by Artificial Intelligence and Deep Machine Learning (Zhang et al., 2018). This will potentially allow for doctors to more accurately recommend the appropriate treatments to their patients, hopefully “relieve the stress of medical malpractice claims on clinicians and practitioners” (Hwang et al., 2018).

 

Data Analysis Can Lead to Better Predictive CareWhat can Artificial Intelligence do for business today?

As more data has gone digital, the healthcare field has vast, limitless troves of information that can only be effectively interpreted and utilized through the power of AI.

A predictive cohort study in the UK, for example, reviewed the medical records of 378,256 patients with AI algorithmic programming (Weng et al., 2017). The results showed an improved predictive rate for cardiovascular events by 7.6% over traditional diagnostic methods. The AI-generated data works in partnership with medical practitioners to provide crucial preventive care earlier and faster than established means.

Medical Science Fact for All Through AI Programming

The Human Diagnosis Project, a global effort that seeks to gather the knowledge and experience of over 7,500 physicians and 500 institutions in 80 countries, promises to bring important medical knowledge to everyone (Nundy, 2018).

 

Through the compilation and analysis of such data by AI generated tools, the project strives to provide critical, healthcare related information to the global community with higher accuracy, affordability, and accessibility. Clinical diagnostics and providing necessary treatments will no longer be limited by borders or local resources.

 

The promise of AI in healthcare will offer the opportunity to live a healthier life for many more people around the globe. Radical and robust machine learning and deep machine learning programs will provide society the necessary leap in accuracy and efficiency in healthcare, while relying on the knowledge, creativity and talent of humans for its proper implementation and integration. As the positive results of AI become more evident every day, the ability to treat patients anywhere, anytime will likely be boon for everyone.

About the Author: 

Rae Steinbach is a freelance editor at Rotheberg LLP. She is a graduate of Tufts University with a combined International Relations and Chinese degree. After spending time living and working abroad in China, she returned to NYC to pursue her career and continue curating quality content. Rae is passionate about travel, food, and writing, of course.

 

Works Cited

Abbasi, J. (2018). Shantanu nundy, md: The human diagnosis project. Jama, 319(4), 329-331.

Bini, S. A.Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? The Journal of Arthroplasty, doi:10.1016/j.arth.2018.02.067

Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., & al, e. (2017). Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama, 318(22), 2199-2210.

Hwang, C. Y., Wu, C. H., Cheng, F. C., Yen, Y. L., & Wu, K. H. (2018). A 12-year analysis of closed medical malpractice claims of the taiwan civil court: A retrospective study. Medicine, 97(13), e0237. doi:10.1097/MD.0000000000010237. doi:MD-D-17-06294 [pii]

Misawa, M., Kudo, S., Mori, Y., Cho, T., Kataoka, S., Yamauchi, A., . . . Mori, K. (2018). Artificial intelligence-assisted polyp detection for colonoscopy: Initial experience. Gastroenterology, 154(8), 2027-2029.e3. doi:10.1053/j.gastro.2018.04.003

Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? Plos One, 12(4), e0174944.

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