APPLICATION OF ARTIFICIAL INTELLIGENCE IN MEDICAL EDUCATION AND MEDICAL DIAGNOSTICS

Elyor Valitov

Fergana Medical Institute of Public Health, Assistant of the Department of Biophysics and Information Technology of Biomedical Engineering, Fergana

Keywords: artificial intelligence, medicine, education, diagnostics, synopsis.


Abstract

The field of artificial intelligence (AI) is a recent addition to technology. Its goal is to simulate, extend, and expand human intellect via the study and development of theory, method, technique, and application system using computer technology. New artificial intelligence technologies have brought about significant changes to the traditional medical setting. For instance, a patient's diagnosis derived from biochemical, endoscopic, ultrasonographic, radiographic, and pathological exams has been successfully advanced with reduced human workload and increased accuracy. Better surgical outcomes have significantly improved the medical care provided during the perioperative phase, which includes preoperative planning, surgery, and postoperative recuperation. AI technology has also significantly influenced the development of medicinal drugs and changed the course of medical administration, teaching, and research. This review's objectives are to outline the use of AI in medicine and offer a forecast for the next developments.


References

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

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

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

Shortliffe EH, Davis RM, Axline SG, et al. The MYCIN system enables computer-based consultations in clinical treatments, including rule acquisition and explanation. Comput Biomed Res, 1975, 8(4):303–320.

Weiss S, Kulikowski CA, Safir A. Glaucoma consultation by computer. Comput Biol Med. 1978;8(1):25-40. Kulikowski CA. Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art, with Reflections on Current AIM Challenges. Yearb Med Inform, 2019, 28(1): 249–256.

Stefanelli M, Shortliffe EH, and Patel VL. The rise of artificial intelligence in medicine. Artif Intell Med. 2009;46(1):5-17. Poncette AS, Mosch L, Spies C, et al. Improved Patient Monitoring in the Intensive Care Unit: A Survey Study. J Med Internet Res, 2020;22(6):e19091.

Zandvliet AS, Haldna L, and Angehrn Z. Artificial intelligence and machine learning are used at the point of care. Front Pharmacol, 2020, 11:759. Dai B, Yu Y, Huang L, et al. Use of a neural network model to aid with device fitting for low vision patients. Ann Transl Med, 2020;8(11):702.

Averta G, Della S, Valenza G, et al. Exploiting upper limb functional main components to generate human-like mobility in anthropomorphic robots. J Neuroeng Rehabil, 2020;17(1):63.

Zhao Y, Liang C, Gu Z, et al. A novel design scheme for an intelligent upper limb rehabilitation training robot. International Journal of Environmental Research and Public Health, 2020, 17(8): 2948.

DeCH, Corradi F, Smeets C, et al. Wearable monitoring and interpretable machine learning can objectively track patients' progress throughout cardiac rehabilitation. Sensors (Basel), 2020, 20(12): 3601.

Ramezani R, Zhang W, Xie Z, et al. Baseline study results on using indoor localization and wearable sensor-based physical activity recognition to assess older patients undergoing subacute rehabilitation. JMIR MHealth Uhealth, 2019, 7(7):e14090.

Tae K. Robotic thyroid surgery. Auris Nasus Larynx, 2020;48(3):331-338. Stefanelli L, Mandelaris GA, Franchina A, et al. A Case Study of the Accuracy of 14 Maxillary Full Arch Implant Treatments Performed with the Da Vinci Bridge. Materials (Basel), 2020;13(12):2806.

Lenfant L, Wilson C, Sawczyn G, et al. SinglePort Robot-Assisted Dismembered Pyeloplasty Using Mini-Pfannenstiel or Peri-Umbilical Access: First Results in a Single Center. Urology, 2020, 143:147–152.

A. Winder, D. Strauss, R. L. Jones, et al. A case series from a single hospital on robotic surgery for stomach gastrointestinal stromal tumors. J Surg Oncol, 2020; doi: 10.1002/jso.26053. Online ahead of print.

Jones R, Dobbs RW, Halgrimson W, et al. Single port robotic radical prostatectomy using the da Vinci SP platform: a step-by-step guide. Can J Urol, 2020, 27(3):10263–10269.

Lenfant L, Wilson C, Sawczyn G, et al. SinglePort Robot-Assisted Dismembered Pyeloplasty Using Mini-Pfannenstiel or Peri-Umbilical Access: First Results in a Single Center. Urology, 2020, 143:147–152.

A. Winder, D. Strauss, R. L. Jones, et al. A case series from a single hospital on robotic surgery for stomach gastrointestinal stromal tumors. J Surg Oncol, 2020; doi: 10.1002/jso.26053. Online ahead of print.

Jones R, Dobbs RW, Halgrimson W, et al. Single port robotic radical prostatectomy using the da Vinci SP platform: a step-by-step guide. Can J Urol, 2020, 27(3):10263–10269.

Lenfant L, Wilson C, Sawczyn G, et al. SinglePort Robot-Assisted Dismembered Pyeloplasty Using Mini-Pfannenstiel or Peri-Umbilical Access: First Results in a Single Center. Urology, 2020, 143:147–152.

A. Samareh, X. Chang, W.B. Lober, et al. AI Methods for Detection, Monitoring, and Decision Making in Surgical Site Infection. Surg Infect (Larchmt), 20(7):546–554, 2019.

Cheng N, Kuo A. LSTM Neural Networks for Predicting Emergency Department Wait Time. Stud Health Technol Inform, 2020; 272:199-202.

Nas S, Koyuncu M. Emergency Department Capacity Planning using Recurrent Neural Networks and Simulations. Computer Math Methods Med (2019): 4359719.

Saab A, Saikali M, & Lamy JB. A comparison of machine learning methods for predicting adverse event-related 30-day hospital readmissions and their implications for patient safety. Stud Health Technol Inform, 2020, 272: 51–54.

Lin YW, Zhou Y, Faghri F, et al. We employed recurrent neural networks with long short-term memory to investigate and predict unplanned intensive care unit readmissions. PLoS One, 2019, 14(7): e218942.

Wu D, Xiang Y, Wu X, et al. AI tutoring for problem-based learning during ophthalmology clerkship. Ann Transl Med. 2020;8(11):700.

Yang YY; Shulruf B. A prospective pilot study discovered that an expert-led and AI-assisted coaching course boosted confidence in suturing and ligature skills among Chinese medical trainees. J Educ Eval Health Prof. 2019;16:7.

Mirchi, N.; Bissonette, V.; Yilmaz, R. The Virtual Operative Assistant is a user-friendly artificial intelligence platform for teaching surgery and medicine through simulations. PLoS One, 2020; 15(2): e229596.

Bertin H, Huon J, Praud M, et al. 3D printed mandible models allow maxillofacial surgery trainees to perform bilateral sagittal split osteotomies. Br J Oral Maxillofac Surg, 2020;58(8):953–958.

Bohl M, McBryan S, Pais D, et al. The Living Spine Model: A Biomimetic Surgical Training and Education Tool. Oper Neurosurg (Hagerstown), 2020, 19(1):98–106.

Sappenfield JA, Smith WB, Cooper LA, et al. Visualization improves supraclavicular access to the subclavian vein in a mixed-reality simulator. Anesth Analg, 2018;127(1):83–89.

Namikawa K, Hirasawa T, Yoshio T, et al. AI in Endoscopy: A Clinician's Guide. Expert Review Gastroenterol Hepatol, 2020: 1-18.

Hwang Y, Lee HH, Park CS, et al. Improved Small Bowel Capsule Endoscopy Classification and Localization using Convolutional Neural Networks. Dig Endosc, 2020, 33(4): 598–607

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

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

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

A bright future for robotic surgery // https://openmedscience.com/bright-future-for-robotic-surgeons/

HeartLander // https://www.cs.cmu.edu/~heartlander/index.html

Tejo OA, Buj CI, and Fenollosa AF. A review of 3D printing in medicine for preoperative surgical planning. Ann Biomed Eng, 2020, 48(2): 536–555.

Wang C, Zhang L, Qin T, et.al. A comprehensive study of 3D printing for adult cardiovascular surgery and procedures. J Thorac Dis, 2020, 12(6): 3227–3237.

Nikoyan and Patel discuss the use of intraoral scanners, three-dimensional imaging, and printing in dental offices. Dent Clin North Am, 2020, 64(2):365–378.

Skelley NW, Smith MJ, Ma RM, et al. 3D Printing Technology in Orthopaedics. J Am Acad Orthop Surg, 2019, 27(24): 918–925.

Yamaguchi JT, Hsu WK. 3D Printing for Minimally Invasive Spine Surgery. Curr Rev Musculoskelet Med, 2019, 12(4):425-435

Bangeas, P., Tsioukas, V., & Papadopoulos, N. Innovative 3D printing models help control hepatobiliary cancers. World J Hepatol, 2019;11(7):574-585.

Feng ZH, Li XB, Phan K, et al. Creating a 3D navigation template to guide screw trajectory in the spine using Mimics and 3-Matic software. J Spine Surg, 2018, 4(3): 645–653.

Kashyap A, Kadur S, Mishra A, et al. present a novel cervical pedicle screw guiding jig. J Clin Orthop Trauma, 2018; 9(3): 226-229.

Edgar L, Pu T, Porter B, et al. "Regenerative medicine, organ bioengineering, and transplantation." Br J Surg. 2020;107(7):793-800.