The use of artificial intelligence in different medical branches: An overview of the literature

The goal of this study is to help create a perspective about usage of Artificial Intelligence in some branches of medicine as oncology, radiology, surgery, emergency medicine, etc. A literature review of “Artificial Intelligence in medicine” and for the use of artificial intelligence in some medical branches has been done. Radiology is one of the most notable in the artificial intelligence field and open to many developments in this field. Ten millions of radiology reports and billions of images are now digitally accumulated, simplifying the “big data” concept and creating the bottom line for Artificial Intelligence research. Pathologists used Artificial Intelligence to reduce the error rate of diagnosis of cancer-positive lymph nodes. The accuracy of cancer prediction results has increased considerably in 15-20% of recent years with the application of Machine Learning techniques. Two deep learning systems trained to detect and treat diabetic retinopathy and macular edema achieved high specificities (98%) and sensitivity (87% - 90%) to detect moderate retinopathy and macular edema using a large of retinal photographs in ophthalmology. Several Machine Learning models have promised to develop current triage methods in the Emergency Departments. Surgeons will likely see the Artificial Intelligence analysis of the population and patient-specific data in the future. Artificial Intelligence can certainly help doctors make better clinical decisions and judgments in certain functional areas in health care.

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1. Patel VT, Shortliffe EH, Stefanelli M, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med. 2009;46:5-17.

2. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;247:36-40.

3. Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med. 2018;131:129-33.

4. Wang D, Khosla A, Gargeya R, et al. Deep learning for identifying breast cancer. In: Proceedings of the International Society on Biomedical Imaging (ISBI). Quantitative Methods, 2016.

5. Esteva A, Kuppel B, Novoa RN, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115-9.

6. Hashimoto DA, Rosman G, Rus D, et al. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268:70-6.

7. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402-10.

8. Walton OB, Garoon RB, Weng CY, et al. Evaluation of automated teleretinal screening program for diabetic retinopathy. JAMA Ophthalmol. 2016;134:204-9.

9. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present, and future. Stroke Vascul Neurol. 2017;2:230-43.

10. Ye H, Shen H, Dong Y, et al. Using evidence-based medicine through advanced data analytics to work toward a national standard for hospital-based acute ischemic stroke treatment. Mainland China, 2017.

11. Thrall JH, Li X, Li Q, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 2018;15:504-8.

12. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216-9.

13. Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care. J Arthroplasty. 2018;33:2358-61.

14. Kourou K, Exarchos TP, Exarchos KP, et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2014;13:8-17.

15. Senders JT, Arnaout O, Karhade AV, et al. Natural and artificial intelligence in neurosurgery: a systemic review. Neurosurgery. 2017;83:181-92

16. Berlyand Y, Raja AS, Dorner SC, et al. How artificial intelligence could transform emergency department operations. Am J Emerg Med. 2018;36:1515-7.

17. Alessandra CF. A new era of oncology through artificial intelligence. ESMO Open. 2017;2:1-2.
Medicine Science-Cover
  • ISSN: 2147-0634
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2012
  • Yayıncı: Effect Publishing Agency ( EPA )
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