These innovations although positive raise great concerns about the storage of sensitive medical information. If the data is misused it can introduce privacy risks. Especially if the data is mishandled by corrupt corporate companies or insurers. Artificial intelligence systems can also accidentally inherit a bias from their training. This can lead to unequal care for disadvantage communities.This can be concerning.

I personally see a great potential for Artificial intelligence growth to bring help to other underserved regions. I am also greatly concerned about over reliance on artificial intelligence algorithms in decision making. It can be difficult to balance innovation with a strict ethical framework but this is very important in the healthcare industry to make sure everyone receives fair treatment, and optimal care.

References

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z

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(4), 230–243. https://doi.org/10.1136/svn-2017-000101

Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22–28. https://doi.org/10.1177/0141076818815510

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G. S., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z

Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318. https://doi.org/10.1001/jama.2017.18391

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181

Davenport, T., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world

Another concern is job displacement because of the transformation of AI in our society. This leaves careers with different roles. It can be concerning for professions like radiologists, transcriptionists, scribes, and other roles impacted by automation. It also leaves a good opportunity for new Opportunities to develop in AI development. This requires the reshaping and reskilling of the workforce. Privacy risk remain critical. As AI data sets are constantly. Depending on extensive medical information, not known to the average public.

As a future healthcare professional, I am excited to use artificial intelligence in the workplace. I believe ethical deployment is important though to ensure that no group is marginalized. It is important to balance, AI and human professional input to ensure the best care is given possible.

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(4), 230–243. https://doi.org/10.1136/svn-2017-000101

Mak, K. K., & Pichika, M. R. (2018). Artificial intelligence in drug development: Present status and future prospects. Current Opinion in Pharmacology, 42, 81–89. https://doi.org/10.1016/j.coph.2018.06.004

National Cancer Institute. (n.d.). Definition of personalized medicine. U.S. Department of Health and Human Services. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/personalized-medicine

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Artificial Intelligence makes this possible. Modern healthcare has a massive amount of information like lab test, data sequences, imaging scans and wearable data. It would be impossible for humans only to analyze alone. For example, Artificial Intelligence can determine which cancer therapy is more likely to benefit a patient based on their genetic profile or it can predict an individual’s risk for developing heart disease or other certain conditions.Using biometrics and information.

Other than treatment selection, AI drives drug discovery by showcasing how certain compounds can interact with genetic variations. Significantly reducing time and costs in comparison to other models. Artificial intelligence enables continuous real time patient monitoring and management. This allows treatments to be dynamically adjusted as health data alters.

By using predictive programming analytics with individualized data. Artificial intelligence driven personalized medicine defines that promise as a future healthcare professional to always give the best optimal care. Making medicine more efficient and patient centered. I think it’s amazing for the future of medicine and healthcare.

Jiang, Fei, et al. “Artificial Intelligence in Healthcare: Past, Present and Future.” Stroke and Vascular Neurology, vol. 2, no. 4, 2017, pp. 230–243.

Mak, K. K., and M. R. Pichika. “Artificial Intelligence in Drug Development.” Current Opinion in Pharmacology, vol. 42, 2018, pp. 81–89.

National Cancer Institute. “Definition of Personalized Medicine.” cancer.gov, U.S. Department of Health and Human Services.

Topol, Eric. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019.