Skip to content
naulon

No retraction, correction or concern found when last checked on Oct 31, 2026.

Application of Artificial Intelligence in Medicine: An Overview

Peng-ran Liu, Lin Lu, Jia-yao Zhang and 3 more

Current Medical Science · 2021 · Review · CC BY

doi:10.1007/s11596-021-2474-3

Abstract

Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient’s diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

References 142

  1. 1.

    Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73-81.

    Not in our library10.1080/13645706.2019.1575882

  2. 2.

    Kaul V, Enslin S, Gross SA. The history of artificial intelligence in medicine. Gastrointest Endosc. 2020;92(4):807-812.

    Not in our library10.1016/j.gie.2020.06.040

  3. 3.

    Miller RA, Pople HJ, Myers JD. Internist-1, an experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med. 1982;307(8):468-476.

    Not in our library10.1056/nejm198208193070803

  4. 4.

    Shortliffe EH, Davis R, Axline SG, et al. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res. 1975;8(4):303-320.

    Not in our library10.1016/0010-4809(75)90009-9

  5. 5.

    Weiss S, Kulikowski CA, Safir A. Glaucoma consultation by computer. Comput Biol Med. 1978;8(1):25-40.

    Not in our library10.1016/0010-4825(78)90011-2

  6. 6.

    Kulikowski CA. Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art — with Reflections on Present AIM Challenges. Yearb Med Inform. 2019;28(1):249-256.

    Not in our library10.1055/s-0039-1677895

  7. 7.

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

    Not in our library10.1016/j.artmed.2008.07.017

  8. 8.

    Sorrentino FS, Jurman G, De NK, et al. Application of Artificial Intelligence in Targeting Retinal Diseases. Curr Drug Targets. 2020;21(12):1208-1215.

    Not in our library10.2174/1389450121666200708120646

  9. 9.

    Heydon P, Egan C, Bolter L, et al. Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol. 2021;105(5):723-728.

    Not in our library10.1136/bjophthalmol-2020-316594

  10. 10.

    Xie Q, Liu Y, Huang H, et al. An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms. Pharmacol Res. 2020;159:104986.

    Not in our library10.1016/j.phrs.2020.104986

  11. 11.

    Gong J, Liu JY, Sun XW, et al. Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules. Phys Med Biol. 2018;63(3):35036.

    Not in our library10.1088/1361-6560/aaa610

  12. 12.

    Gong J, Liu JY, Jiang YJ, et al. Fusion of quantitative imaging features and serum biomarkers to improve performance of computer-aided diagnosis scheme for lung cancer: A preliminary study. Med Phys. 2018;45(12):5472-5481.

    Not in our library10.1002/mp.13237

  13. 13.

    Rodriguez RA, Lang K, Gubern MA, et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019;111(9):916-922.

    Not in our library10.1093/jnci/djy222

  14. 14.

    McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.

    Not in our library10.1038/s41586-019-1799-6

  15. 15.
  16. 16.

    Stoel BC. Artificial intelligence in detecting early RA. Semin Arthritis Rheum. 2019;49(3S):S25-S28.

    Not in our library10.1016/j.semarthrit.2019.09.020

  17. 17.

    Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288(1):62-81.

    Not in our library10.1111/joim.13030

  18. 18.

    Allen TC. Regulating Artificial Intelligence for a Successful Pathology Future. Arch Pathol Lab Med. 2019;143(10):1175-1179.

    Not in our library10.5858/arpa.2019-0229-ed

  19. 19.

    Bera K, Schalper KA, Rimm DL, et al. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703-715.

    Not in our library10.1038/s41571-019-0252-y

  20. 20.
  21. 21.

    Zemouri R, Devalland C, Valmary DS, et al. Neural network: A future in pathology? Ann Pathol. 2019;39(2):119-129.

    Not in our library10.1016/j.annpat.2019.01.004

  22. 22.

    Wang S, Yang DM, Rong R, et al. Pathology Image Analysis Using Segmentation Deep Learning Algorithms. Am J Pathol. 2019;189(9):1686-1698.

    Not in our library10.1016/j.ajpath.2019.05.007

  23. 23.

    Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 2019;475(2):131-138.

    Not in our library10.1007/s00428-019-02594-w

  24. 24.

    Hart S N, Flotte W, Norgan AP, et al. Classification of Melanocytic Lesions in Selected and Whole-Slide Images via Convolutional Neural Networks. J Pathol Inform. 2019;10:5.

    Not in our library10.4103/jpi.jpi_32_18

  25. 25.

    Kosaraju SC, Hao J, Koh HM, et al. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis. Methods. 2020;179:3-13.

    Not in our library10.1016/j.ymeth.2020.05.012

  26. 26.

    Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24(10):1559-1567.

    Not in our library10.1038/s41591-018-0177-5

  27. 27.
  28. 28.
  29. 29.
  30. 30.

    Jiang YQ, Xiong JH, Li HY, et al. Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network. Br J Dermatol. 2020;182(3):754-762.

    Not in our library10.1111/bjd.18026

  31. 31.

    Bueno G, Fernandez CM, Gonzalez L, et al. Glomerulosclerosis identification in whole slide images using semantic segmentation. Comput Methods Programs Biomed. 2020;184:105273.

    Not in our library10.1016/j.cmpb.2019.105273

  32. 32.

    Namikawa K, Hirasawa T, Yoshio T, et al. Utilizing artificial intelligence in endoscopy: a clinician’s guide. Expert Rev Gastroenterol Hepatol, 2020:1–18

    Not in our library10.1080/17474124.2020.1779058

  33. 33.

    Gulati S, Emmanuel A, Patel M, et al. Artificial intelligence in luminal endoscopy. Ther Adv Gastrointest Endosc. 2020;13:2631774520935220.

    Not in our library10.1177/2631774520935220

  34. 34.

    Hwang Y, Lee HH, Park C, et al. An Improved Classification and Localization Approach to Small Bowel Capsule Endoscopy Using Convolutional Neural Network. Dig Endosc. 2020;33(4):598-607.

    Not in our library10.1111/den.13787

  35. 35.

    He YS, Su JR, Li Z, et al. Application of artificial intelligence in gastrointestinal endoscopy. J Dig Dis. 2019;20(12):623-630.

    Not in our library10.1111/1751-2980.12827

  36. 36.

    Chahal D, Byrne MF. A primer on artificial intelligence and its application to endoscopy. Gastrointest Endosc. 2020;92(4):813-820.

    Not in our library10.1016/j.gie.2020.04.074

  37. 37.

    Sharma P, Pante A, Gross SA. Artificial intelligence in endoscopy. Gastrointest Endosc. 2020;91(4):925-931.

    Not in our library10.1016/j.gie.2019.12.018

  38. 38.

    Nguyen DT, Pham TD, Batchuluun G, et al. Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains. J Clin Med. 2019;8(11):1976.

    Not in our library10.3390/jcm8111976

  39. 39.

    Nguyen DT, Kang JK, Pham TD, et al. Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence. Sensors (Basel). 2020;20(7):1822.

    Not in our library10.3390/s20071822

  40. 40.

    Sun C, Zhang Y, Chang Q, et al. Evaluation of a deep learning-based computer-aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images. Med Phys. 2020;47(9):3952-3960.

    Not in our library10.1002/mp.14301

All 142 references

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.