AMEI's Current Trends in Diagnosis & Treatment

Register      Login

VOLUME 5 , ISSUE 2 ( July-December, 2021 ) > List of Articles


Role of Artificial Intelligence in Diagnosis and Treatment of Various Medical Diseases in Patients

Rahat Kumar, Avlokita Sharma, Pratyush Sharma, Richa Thaman

Keywords : Algorithms, Artificial intelligence, Machine intelligence, Therapeutics

Citation Information : Kumar R, Sharma A, Sharma P, Thaman R. Role of Artificial Intelligence in Diagnosis and Treatment of Various Medical Diseases in Patients. Curr Trends Diagn Treat 2021; 5 (2):92-98.

DOI: 10.5005/jp-journals-10055-0131

License: CC BY-NC 4.0

Published Online: 08-12-2021

Copyright Statement:  Copyright © 2021; The Author(s).


Artificial intelligence (AI) is defined as the capability of a machine to imitate intelligent human behavior in general. With a tremendous rise in computer capability the artificial intelligence by using various algorithms is helpful in helping medical experts for better diagnosis and treatment. Humans’ mind first plans a goal and then requires AI to achieve this goal through supervised and unsupervised learning. The various algorithms used in AI are the artificial neural network, k-nearest neighbor, support vector machine, decision trees, regression analysis classifiers, Bayesian network, random forest, discriminant analysis. AI has various benefits as in breast cancer diagnosis and staging in whole-slide images histopathology study on lung adenocarcinoma and squamous cell carcinoma patients, faster interpretation, and diagnosis in the medical fields in quick diagnosis and treatment of cardiovascular disorders, psychiatric disorders, gastroenterology, surgery, ophthalmology, etc. The more useful is the interpretation and planning of the regimens for cancer diagnosis and treatment. However, AI lacks holistic approach of management and so can never replace treatment by humane methods but AI can be a useful supplement for doctors for planning therapeutics.

  1. Turing AM. Computing machinery and intelligence. Mind 1950;59:433–460.
  2. Definition “Artificial Intelligence.” Available from: [Last accessed on August 31, 2021].
  3. 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. Journal of the American Medical Association 2016;316(22):2402–2410. DOI: 10.1001/jama.2016.17216.
  4. CB Insights Research. Healthcare remains the hottest AI category for deals. 2017. Available from: [Last accessed on August 31, 2021].
  5. McClelland C. The difference between artificial intelligence, machine learning, and deep learning; 2017. Available from: [Last accessed on April 30, 2020].
  6. Senders JT, Arnaout O, Karhade AV, et al. Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery 2018;83(2):181–192. DOI: 10.1093/neuros/nyx384.
  7. Choy G, Khalilzadeh O, Michalski M, et al. Current applications and future impact of machine learning in radiology. Radiology 2018;288(2):318–328. DOI: 10.1148/radiol.2018171820.
  8. Okuboyejo DA, Olugbara OO. A review of prevalent methods for automatic skin lesion diagnosis. Open Dermatol J 2018;12(1):14–53. DOI: 10.2174/187437220181201014.
  9. A beginner's guide to neural networks and deep learning. Available from: [Last accessed on April 30, 2020].
  10. Kohli M, Prevedello LM, Filice RW, et al. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol 2017;208(4):754–760. DOI: 10.2214/AJR.16.17224.
  11. Jakkula V. Tutorial on support vector machine (SVM). Available from: [Last accessed on August 31, 2021].
  12. Craft JA 3rd. Artificial intelligence and the softer side of medicine. Mo Med 2018;115(5):406–409. PMID: 30385982.
  13. Pun T, Gerig G, Ratib O. Image analysis and computer vision in medicine. Comput Med Imaging Graph 1994;18(2):85–96. DOI: 10.1016/0895-6111(94)90017-5.
  14. Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature 2015;521(7553):452–459. DOI: 10.1038/nature14541.
  15. Krittanawong C, Zhang H, Wang Z, et al. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol 2017;69(21): 2657–2664. DOI: 10.1016/j.jacc.2017.03.571.
  16. Beneke F, Mackenrodt MO. Artificial intelligence and collusion. IIC–Int Rev Intellect Prop Compet Law 2019;50:109–134. DOI: 10.1007/S40319-018-00773-X.
  17. Kalyane D, Sanap G, Paul D, et al. Artificial intelligence in the pharmaceutical sector: current scene and future prospect. In: Tekade RK, editor. The future of pharmaceutical product development and research. Elsevier; 2020. p. 73–107.
  18. Medsker L, Jain LC. Recurrent neural networks: design and applications. CRC Press; 1999.
  19. Bielecki A, Bielecki A. Foundations of artificial neural networks. In: Kacprzyk J, editor. Models of neurons and perceptrons: selected problems and challenges. Springer International Publishing; 2019. p. 15–28.
  20. Vyas M, Thakur S, Riyaz B, et al. Artificial intelligence: the beginning of a new era in pharmacy profession. Asian J Pharm 2018;12(2): 72–76.
  21. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553): 436–444. DOI: 10.1038/nature14539.
  22. Ferrucci D, Levas A, Bagchi S, et al. Watson: beyond Jeopardy! Artif Intell 2012;199(200):93–105. DOI: 10.1016/j.artint.2012.06.009.
  23. Bakkar N, Kovalik T, Lorenzini I, et al. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA_binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol 2018;135(2):227–247. DOI: 10.1007/s00401-017-1785-8.
  24. Bejnordi BE, Veta M, van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastasis in women with breast cancer. Journal of the American Medical Association 2017;318(22):2199–2210. DOI: 10.1001/jama.2017.14585.
  25. Yu KH, Zhang C, Berry GI, et al. Predicting nonsmall cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun 2016;7:12474. DOI: 10.1038/ncomms12474.
  26. Choi E, Schuetz A, Stewart WF, et al. Using recurrent neural network models for early detection of heart failure. J Am MEd Inform Assoc 2017;24(2):361–370. DOI: 10.1093/jamia/ocw112.
  27. Buzeav IV, Plechev V, Nikolaeva IE, et al. Artificial intelligence: neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes. Chronic Dis Transl Med 2016;2(3):166–172. DOI: 10.1016/j.cdtm.2016.09.007.
  28. Stein DJ, Lund C, Nesse RM. Classification systems in psychiatry: diagnosis and global mental health in the era of DSM-5 and ICD-11. Curr Opin Psychiatry 2013;26(5):493–497. DOI: 10.1097/YCO.0b013e3283642dfd.
  29. Wakefield JC. Diagnostic issues and controversies in DSM-5: return of the false positives problem. Annu Rev Clin Psychol 2016;12:105–132. DOI: 10.1146/annurev-clinpsy-032814-112800.
  30. Bedi G, Carrillo F, Cecchi GA, et al. Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophr 2015;1:15030. DOI: 10.1038/npjschz.2015.30.
  31. Elvevag B, Foltz PW, Rosenstein M, et al. An automated method to analyze language use in patients with schizophrenia and their first-degree relatives. J Neurolinguistics 2010;23(3):270–284. DOI: 10.1016/j.jneuroling.2009.05.002.
  32. Detrano R, Guerci A, Carr JJ, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. New Engl J Med 2008;358(13):1336–1345. DOI: 10.1056/NEJMoa072100.
  33. Kanesaka T, Lee T, Uedo N, et al. Computer-aided diagnosis for identifying early gastric cancers in magnifying narrow band images. Gastrointest Endosc 2017;87(5):1339–1344. DOI: 10.1016/j.gie.2017.11.029.
  34. Szold A, Bergamaschi R, Broeders I, et al. European Association of Endoscopic Surgeons European Association of Endoscopic Surgeons (EAES) Consensus statement on the use of robotics in general surgery. Surg Endosc 2015;29(2):253–288. DOI: 10.1007/s00464-014-3916-9.
  35. Precision medicine that only human + AI can achieve. Reinventing imaging so you can practice better and faster. Arterys Website. Available from: [Last accessed on August 31, 2021].
PDF Share
PDF Share

© Jaypee Brothers Medical Publishers (P) LTD.