Implementation of digital technologies in the medicine of the future

Authors

DOI:

https://doi.org/10.57125/FEM.2022.06.30.02

Keywords:

artificial intelligence, machine learning, radiomics, telemedicine, computer model, healthcare

Abstract

The parallel development of digital and telecommunication technologies in recent years has created ample opportunities to improve traditional healthcare delivery systems. New models of healthcare services are supported by scientific developments and digital innovations.

Objectives: To improve doctors’ expertise and healthcare managers with the main achievements of digital technologies introduction in practical medicine and the possibilities for further development of technologies in this area.

Methodology:  A review of articles from professional peer-reviewed journals in the online medical literature search system MEDLINE from the US National Library of Medicine for 2018-2022 was conducted. The article concerns the use of digital innovations in clinical medicine, their achievements in oncology, cardiology, medical imaging and other healthcare fields.

Results:  The article discusses the main digital strategies that are part of the practical work of doctors and create new workflows and interactions between patients and specialists of medical institutions. Attention is paid to the advances in computer technology and the challenges they pose to the quality of medical care. The introduction of telemedicine, problems of validation of results and clinical use of artificial intelligence applications, as well as opportunities for their development in the future are discussed.

Conclusion: New technologies, including artificial intelligence algorithms, machine learning, image recognition, radiomics, and telemedicine, are creating the prerequisites for the development of new forms of healthcare delivery. The combination of telemedicine, server-based applications, and machine learning algorithms can provide a synchronised solution to the challenges faced by modern society in ensuring a healthy future around the world.

References

Fogel AL, Kvedar JC. Artificial intelligence powers digital medicine. NPJ Digit Med. 2018 Mar 14;1:5. doi: 10.1038/s41746-017-0012-2.

He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019 Jan;25(1):30-36. doi: 10.1038/s41591-018-0307-0.

Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci. 2021 Dec;41(6):1105-1115. doi: 10.1007/s11596-021-2474-3.

Niazi MK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019 May;20(5):e253-e261. doi: 10.1016/S1470-2045(19)30154-8.

Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci. 2020 May;111(5):1452-1460. doi: 10.1111/cas.14377.

Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021 Apr;11(4):900-915. doi: 10.1158/2159-8290.CD-21-0090.

Ibrahim A, Gamble P, Jaroensri R, Abdelsamea MM, Mermel CH, Chen PC et al. Artificial intelligence in digital breast pathology: Techniques and applications. Breast. 2020 Feb;49:267-273. doi: 10.1016/j.breast.2019.12.007.

Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020 Feb;132(2):379-394. doi: 10.1097/ALN.0000000000002960.

Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M et al. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018 Jun 12;71(23):2668-2679. doi: 10.1016/j.jacc.2018.03.521.

Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C et al. Artificial intelligence in reproductive medicine. Reproduction. 2019 Oct;158(4):R139-R154. doi: 10.1530/REP-18-0523.

Fang YT, Lan Q, Xie T, Liu YF, Mei SY, Zhu BF. New Opportunities and Challenges for Forensic Medicine in the Era of Artificial Intelligence Technology. Fa Yi Xue Za Zhi. 2020 Feb;36(1):77-85. doi: 10.12116/j.issn.1004-5619.2020.01.016.

Adams SJ, Henderson RD, Yi X, Babyn P. Artificial Intelligence Solutions for Analysis of X-ray Images. Can Assoc Radiol J. 2021 Feb;72(1):60-72. doi: 10.1177/0846537120941671.

Lewis SJ, Gandomkar Z, Brennan PC. Artificial Intelligence in medical imaging practice: looking to the future. J Med Radiat Sci. 2019 Dec;66(4):292-295. doi: 10.1002/jmrs.369.

Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J et al. Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group. Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. Can Assoc Radiol J. 2018 May;69(2):120-135. doi: 10.1016/j.carj.2018.02.002.

Yang W, Wang F. Multislice Spiral Computed Tomography Postprocessing Technology in the Imaging Diagnosis of Extremities and Joints. Comput Math Methods Med. 2021 Dec 13; 2021:9533573. doi: 10.1155/2021/9533573.

Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner SH, Sechopoulos I et al. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2019 Feb;290(2):305-314. doi: 10.1148/radiol.2018181371.

Schaffter T, Buist DS, Lee CI, Nikulin Y, Ribli D, Guan Y et al. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw Open. 2020 Mar 2;3(3):e200265. doi: 10.1001/jamanetworkopen.2020.0265.

Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH et al. Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs. JAMA Netw Open. 2019 Mar 1;2(3):e191095. doi: 10.1001/jamanetworkopen.2019.1095.

Yoo H, Lee SH, Arru CD, Doda Khera R, Singh R, Siebert S et al. AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset. Eur Radiol. 2021 Dec;31(12):9664-9674. doi: 10.1007/s00330-021-08074-7.

Zhang M, Young GS, Chen H, Li J, Qin L, McFaline-Figueroa JR et al. Deep-Learning Detection of Cancer Metastases to the Brain on MRI. J Magn Reson Imaging. 2020 Oct;52(4):1227-1236. doi: 10.1002/jmri.27129.

Azimi P, Shahzadi S, Sadeghi S. Use of artificial neural networks to predict the probability of developing new cerebral metastases after radiosurgery alone. J Neurosurg Sci. 2020 Feb;64(1):52-57. doi: 10.23736/S0390-5616.16.03479-2.

Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am. 2021 Nov;59(6):987-1002.doi: 10.1016/j.rcl.2021.07.001.

Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K et al. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. J Am Coll Radiol. 2018 Mar;15(3 Pt B):504-508. doi: 10.1016/j.jacr.2017.12.026.

Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P et al. Introduction to Radiomics. J Nucl Med. 2020 Apr;61(4):488-495. doi: 10.2967/jnumed.118.222893.

Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018 Nov 14;2(1):36.doi: 10.1186/s41747-018-0068-z.

Park HJ, Park B, Lee SS. Radiomics and Deep Learning: Hepatic Applications. Korean J Radiol. 2020 Apr;21(4):387-401. doi: 10.3348/kjr.2019.0752.

Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res. 2021 Feb;10(2):1186-1199. doi: 10.21037/tlcr-20-708.

Yu Y, He Z, Ouyang J, Tan Y, Chen Y, Gu Y et al. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study. EBioMedicine. 2021 Jul;69:103460. doi: 10.1016/j.ebiom.2021.103460.

Wong LM, Ai QY, Zhang R, Mo F, King AD. Radiomics for Discrimination between Early-Stage Nasopharyngeal Carcinoma and Benign Hyperplasia with Stable Feature Selection on MRI. Cancers (Basel). 2022 Jul 14;14(14):3433. doi: 10.3390/cancers14143433.

Bae H, Lee H, Kim S, Han K, Rhee H, Kim DK et al. Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists. Eur Radiol. 2021 Nov;31(11):8786-8796. doi: 10.1007/s00330-021-07877-y.

Feng B, Chen X, Chen Y, Liu K, Li K, Liu X et al. Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule. Eur J Radiol. 2020 Jul;128:109022. doi: 10.1016/j.ejrad.2020.109022.

Jiang YW, Xu XJ, Wang R, Chen CM. Radiomics analysis based on lumbar spine CT to detect osteoporosis. Eur Radiol. 2022 Jan ;32(11):8019-8026. doi: 10.1007/s00330-022-08805-4.

Iyengar JN. Whole slide imaging: The futurescape of histopathology. Indian J Pathol Microbiol. 2021 Jan-Mar;64(1):8-13. doi: 10.4103/IJPM.IJPM_356_20.

Patel A, Balis UG, Cheng J, Li Z, Lujan G, McClintock DS et al. Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review. J Pathol Inform. 2021 Dec 9;12:50. doi: 10.4103/jpi.jpi_66_21.

Hanna MG, Reuter VE, Hameed MR, Tan LK, Chiang S, Sigel C et al. Whole slide imaging equivalency and efficiency study: experience at a large academic center. Mod Pathol. 2019 Jul;32(7):916-928. doi: 10.1038/s41379-019-0205-0.

Mukhopadhyay S, Feldman MD, Abels E, Ashfaq R, Beltaifa S, Cacciabeve NG et al. Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study). Am J Surg Pathol. 2018 Jan;42(1):39-52. doi: 10.1097/PAS.0000000000000948.

Borowsky AD, Glassy EF, Wallace WD, Kallichanda NS, Behling CA, Miller DV et al. Digital Whole Slide Imaging Compared With Light Microscopy for Primary Diagnosis in Surgical Pathology. Arch Pathol Lab Med. 2020 Oct 1;144(10):1245-1253. doi: 10.5858/arpa.2019-0569-OA.

Samuelson MI, Chen SJ, Boukhar SA, Schnieders EM, Walhof ML, Bellizzi AM et al. Rapid Validation of Whole-Slide Imaging for Primary Histopathology Diagnosis. Am J Clin Pathol. 2021 Apr 26;155(5):638-648.doi: 10.1093/ajcp/aqaa280.

Rizzo PC, Girolami I, Marletta S, Pantanowitz L, Antonini P, Brunelli M et al. Technical and Diagnostic Issues in Whole Slide Imaging Published Validation Studies. Front Oncol. 2022 Jun 16;12:918580. doi: 10.3389/fonc.2022.918580.

Lee S, Amgad M, Mobadersany P, McCormick M, Pollack BP, Elfandy H et al. Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers. Cancer Res. 2021 Feb 15;81(4):1171-1177. doi: 10.1158/0008-5472.CAN-20-0668.

Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019 Aug;25(8):1301-1309. doi: 10.1038/s41591-019-0508-1.

Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019 May;20(5):e253-e261. doi: 10.1016/S1470-2045(19)30154-8.

Gioia G, Salducci M. Medical and legal aspects of telemedicine in ophthalmology. Rom J Ophthalmol. 2019 Jul-Sep;63(3):197-207.

Sirintrapun SJ, Lopez AM. Telemedicine in Cancer Care. Am Soc Clin Oncol Educ Book. 2018 May 23;38:540-545. doi: 10.1200/EDBK_200141.

Yedlinsky NT, Peebles RL. Telemedicine Management of Musculoskeletal Issues. Am Fam Physician. 2021 Feb 1;103(3):147-154.

Srinivasan M, Asch S, Vilendrer S, Thomas SC, Bajra R, Barman L et al. Qualitative Assessment of Rapid System Transformation to Primary Care Video Visits at an Academic Medical Center. Ann Intern Med. 2020 Oct 6;173(7):527-535. doi: 10.7326/M20-1814.

Reed ME, Huang J, Graetz I, Lee C, Muelly E, Kennedy C et al. Patient Characteristics Associated With Choosing a Telemedicine Visit vs Office Visit With the Same Primary Care Clinicians. JAMA Netw Open. 2020 Oct ;3(6):e205873. doi: 10.1001/jamanetworkopen.2020.5873.

Hawley CE, Genovese N, Owsiany MT, Triantafylidis LK, Moo LR, Linsky AM et al. Rapid Integration of Home Telehealth Visits Amidst COVID-19: What Do Older Adults Need to Succeed? J Am Geriatr Soc. 2020 Nov;68(11):2431-2439. doi: 10.1111/jgs.16845.

Buvik A, Bergmo TS, Bugge E, Smaabrekke A, Wilsgaard T, Olsen JA. Cost-Effectiveness of Telemedicine in Remote Orthopedic Consultations: Randomized Controlled Trial. J Med Internet Res. 2019 Feb 19;21(2):e11330. doi: 10.2196/11330.

Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018 Aug 1;29(8):1836-1842. doi: 10.1093/annonc/mdy166.

Haggenmüller S, Krieghoff-Henning E, Jutzi T, Trapp N, Kiehl L, Utikal JS et al. Digital Natives' Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study. JMIR Mhealth Uhealth. 2021 Aug 27;9(8):e22909. doi: 10.2196/22909.

Chuchu N, Dinnes J, Takwoingi Y, Matin RN, Bayliss SE, Davenport C et al. Cochrane Skin Cancer Diagnostic Test Accuracy Group. Teledermatology for diagnosing skin cancer in adults. Cochrane Database Syst Rev. 2018 Dec 4;12(12):CD013193. doi: 10.1002/14651858.CD013193.

Farina GL, Orlandi C, Lukaski H, Nescolarde L. Digital Single-Image Smartphone Assessment of Total Body Fat and Abdominal Fat Using Machine Learning. Sensors (Basel). 2022 May 31;22(21):8365. doi: 10.3390/s22218365.

Alsareii SA, Raza M, Alamri AM, AlAsmari MY, Irfan M, Khan U et al. Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study. Sensors (Basel). 2022 Feb 12;22(4):1420. doi: 10.3390/s22041420.

Gichoya JW, Banerjee I, Bhimireddy AR, Burns JL, Celi LA, Chen LC et l. AI recognition of patient race in medical imaging: a modelling study. Lancet Digit Health. 2022 Jun;4(6):e406-e414. doi: 10.1016/S2589-7500(22)00063-2.

Downloads

Published

2022-06-30

How to Cite

Rakhimov, T., & Mukhamediev, M. (2022). Implementation of digital technologies in the medicine of the future. Futurity Medicine, 1(2), 14–25. https://doi.org/10.57125/FEM.2022.06.30.02