Systematic review MedTech, and Artificial Intelligence

Authors

DOI:

https://doi.org/10.56294/sctconf2024789

Keywords:

Artificial Intelligence, Medical Technology (Medtech)

Abstract

Introduction: interest in the development of applications in medicine associated over artificial intelligence has grown substantially, due to the possibility of opening new immersive and interactive experiences that support different areas of diagnosis and treatment in relation to medical education. The Metaverse’s development and artificial intelligence have had a significant uptake in the medical field, with the advancement of technologies such as Big Data, 5G mobile networks and the Internet of Things.

Objective: to explore the areas of medical breakthroughs with the emergence of artificial intelligence.

Methodology: a systematic review was performed. A search was conduct in PUBMED and SCOPUS databases, together with the academic search engine Google Scholar. Search limits: publication period (January 2020 to January 2024) in the English language. Data was collected and analyzed by the Thematic Analysis Technique, using PRISMA Methodology. A total of 226 articles were found, after searching for doubles in the two bases, 75 documents were reviewed.

Results: after performing the Thematic analysis, 3 categories were determined 1. Diagnostic and treatment; 2. Medical education; 3. Public Health and Ethics

Conclusion: integrating virtual reality and augmented reality provides extensive possibilities in education and medical training. Artificial intelligence (AI) has an important role to play in solving the challenges facing healthcare around the world, so it is increasingly being used in various fields of medicine. The metaverse found that Lifelogging and Mirror-world have the potential for being an important part of developing equipment and applications regarding diagnosis and treatment of diseases in various medical specialties. This would include cardiology, ophthalmology, diagnostic imaging, as well as medical education, for simulation-based training. MedTech also considered the patient-centered and evidence-based medicine perspectives to integrate the most reliable scientific knowledge, alongside the clinical experience of healthcare professionals as well as the values, preferences, and particular circumstances of each patient. This aims to arrive at the best medical decision, which is shared with the patient, related to diagnostics and treatments

References

1. Wu TC, Ho CTB. A scoping review of metaverse in emergency medicine. Australas Emerg Care. 2023 Mar 1;26(1):75–83.

2. Machado H, Silva S, Neiva L. Publics’ views on ethical challenges of artificial intelligence: a scoping review. AI and Ethics [Internet]. 2023; Available from: https://doi.org/10.1007/s43681-023-00387-1

3. Machado H, Silva S, Neiva L. Publics’ views on ethical challenges of artificial intelligence: a scoping review. AI and Ethics [Internet]. 2023; Available from: https://doi.org/10.1007/s43681-023-00387-1

4. Strange M, Tucker J. Global governance and the normalization of artificial intelligence as ‘good’ for human health. AI Soc [Internet]. 2023; Available from: https://doi.org/10.1007/s00146-023-01774-2

5. Crompton H, Burke D. Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education [Internet]. 2023;20(1):22. Available from: https://doi.org/10.1186/s41239-023-00392-8

6. Zhang W, Cai M, Lee HJ, Evans R, Zhu C, Ming C. AI in Medical Education: Global situation, effects and challenges. Educ Inf Technol (Dordr) [Internet]. 2023; Available from: https://doi.org/10.1007/s10639-023-12009-8

7. Pedram S, Kennedy G, Sanzone S. Toward the validation of VR-HMDs for medical education: a systematic literature review. Virtual Real [Internet]. 2023;27(3):2255–80. Available from: https://doi.org/10.1007/s10055-023-00802-2

8. Javed AR, Saadia A, Mughal H, Gadekallu TR, Rizwan M, Maddikunta PKR, et al. Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions. Cognit Comput [Internet]. 2023;15(6):1767–812. Available from: https://doi.org/10.1007/s12559-023-10153-4

9. Harris M, Qi A, Jeagal L, Torabi N, Menzies D, Korobitsyn A, et al. A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis. PLoS One. 2019;14(9):e0221339.

10. Wang DD, Qian Z, Vukicevic M, Engelhardt S, Kheradvar A, Zhang C, et al. 3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease. JACC Cardiovasc Imaging. 2021 Jan;14(1):41–60.

11. Garcia-Canadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. Machine Learning in Fetal Cardiology: What to Expect. Fetal Diagn Ther. 2020;47(5):363–72.

12. Pesapane F, Volonté C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging. 2018 Oct;9(5):745–53.

13. Lakhani P, Gray DL, Pett CR, Nagy P, Shih G. Hello World Deep Learning in Medical Imaging. J Digit Imaging. 2018 Jun;31(3):283–9.

14. Tsoi K, Yiu K, Lee H, Cheng HM, Wang TD, Tay JC, et al. Applications of artificial intelligence for hypertension management. J Clin Hypertens (Greenwich). 2021 Mar;23(3):568–74.

15. Al-Arkee S, Mason J, Lane DA, Fabritz L, Chua W, Haque MS, et al. Mobile Apps to Improve Medication Adherence in Cardiovascular Disease: Systematic Review and Meta-analysis. J Med Internet Res. 2021 May;23(5):e24190.

16. Huo CC, Zheng Y, Lu WW, Zhang TY, Wang DF, Xu DS, et al. Prospects for intelligent rehabilitation techniques to treat motor dysfunction. Neural Regen Res. 2021 Feb;16(2):264–9.

17. Zillikens MC, Demissie S, Hsu YH, Yerges-Armstrong LM, Chou WC, Stolk L, et al. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun. 2017 Jul;8(1):80.

18. Li Y, Xiong Z, Zhang M, Hysi PG, Qian Y, Adhikari K, et al. Combined genome-wide association study of 136 quantitative ear morphology traits in multiple populations reveal 8 novel loci. PLoS Genet. 2023 Jul;19(7):e1010786.

19. Arking DE, Pulit SL, Crotti L, van der Harst P, Munroe PB, Koopmann TT, et al. Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization. Nat Genet. 2014 Aug;46(8):826–36.

20. Zwir I, Del-Val C, Hintsanen M, Cloninger KM, Romero-Zaliz R, Mesa A, et al. Evolution of genetic networks for human creativity. Mol Psychiatry. 2022 Jan;27(1):354–76.

21. Xiong Z, Dankova G, Howe LJ, Lee MK, Hysi PG, de Jong MA, et al. Novel genetic loci affecting facial shape variation in humans. Elife. 2019 Nov;8.

22. Chua IS, Gaziel-Yablowitz M, Korach ZT, Kehl KL, Levitan NA, Arriaga YE, et al. Artificial intelligence in oncology: Path to implementation. Cancer Med. 2021 Jun;10(12):4138–49.

23. Jie Z, Zhiying Z, Li L. A meta-analysis of Watson for Oncology in clinical application. Sci Rep. 2021 Mar;11(1):5792.

24. Aggarwal N, Saini BS, Gupta S. Role of Artificial Intelligence Techniques and Neuroimaging Modalities in Detection of Parkinson’s Disease: A Systematic Review. Cognit Comput [Internet]. 2023; Available from: https://doi.org/10.1007/s12559-023-10175-y

25. Mansour RF, Escorcia-Gutierrez J, Gamarra M, Díaz VG, Gupta D, Kumar S. Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images. Neural Comput Appl [Internet]. 2023;35(22):16037–49. Available from: https://doi.org/10.1007/s00521-021-06240-y

26. Delanerolle G, Yang X, Shetty S, Raymont V, Shetty A, Phiri P, et al. Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care. Womens Health (Lond). 2021;17:17455065211018112.

27. Haugg A, Renz FM, Nicholson AA, Lor C, Götzendorfer SJ, Sladky R, et al. Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis. Neuroimage. 2021 Aug;237:118207.

28. Nunes A, Schnack HG, Ching CRK, Agartz I, Akudjedu TN, Alda M, et al. Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group. Mol Psychiatry. 2020 Sep;25(9):2130–43.

29. Sinno AK, Fader AN. Robotic-assisted surgery in gynecologic oncology. Fertil Steril. 2014 Oct;102(4):922–32.

30. Demner-Fushman D, Elhadad N. Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing. Yearb Med Inform. 2016 Nov;(1):224–33.

31. Vasey B, Ursprung S, Beddoe B, Taylor EH, Marlow N, Bilbro N, et al. Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review. JAMA Netw Open. 2021 Mar;4(3):e211276.

32. Pyun KR, Rogers JA, Ko SH. Materials and devices for immersive virtual reality. Nature Reviews Materials 2022 7:11 [Internet]. 2022 Oct 3 [cited 2023 Aug 11];7(11):841–3. Available from: https://www.nature.com/articles/s41578-022-00501-5

33. Gianola S, Castellini G, Biffi A, Porcu G, Fabbri A, Ruggieri MP, et al. Accuracy of pre-hospital triage tools for major trauma: a systematic review with meta-analysis and net clinical benefit. World Journal of Emergency Surgery [Internet]. 2021 Dec 1 [cited 2023 Jul 8];16(1):1–11. Available from: https://link.springer.com/articles/10.1186/s13017-021-00372-1

34. Slater M, Gonzalez-Liencres C, Haggard P, Vinkers C, Gregory-Clarke R, Jelley S, et al. The Ethics of Realism in Virtual and Augmented Reality. Front Virtual Real. 2020 Mar 3;1:512449.

35. de Almería España Guerrero Cuevas U, Aguayo V. Efectos secundarios tras el uso de realidad virtual inmersiva en un videojuego. International Journal of Psychology and Psychological Therapy [Internet]. 2013 [cited 2023 Jul 8];13(2):163–78. Available from: https://www.redalyc.org/articulo.oa?id=56027416002

36. Martirosov S, Bureš M, Zítka T. Cyber sickness in low-immersive, semi-immersive, and fully immersive virtual reality. Virtual Real [Internet]. 2022 Mar 1 [cited 2023 Jul 8];26(1):15. Available from: /pmc/articles/PMC8132277/

37. Won JH, Kim YS. A New Approach for Reducing Virtual Reality Sickness in Real Time: Design and Validation Study. JMIR Serious Games 2022;10(3):e36397 https://games.jmir.org/2022/3/e36397 [Internet]. 2022 Sep 27 [cited 2023 Jul 8];10(3):e36397. Available from: https://games.jmir.org/2022/3/e36397

38. Wüller H, Behrens J, Garthaus M, Marquard S, Remmers H. A scoping review of augmented reality in nursing. BMC Nurs [Internet]. 2019 May 16 [cited 2023 Jul 8];18(1):1–11. Available from: https://link.springer.com/articles/10.1186/s12912-019-0342-2

39. Surer E, Erkayaoğlu M, Öztürk ZN, Yücel F, Bıyık EA, Altan B, et al. Developing a scenario-based video game generation framework for computer and virtual reality environments: a comparative usability study. Journal on Multimodal User Interfaces [Internet]. 2021 Dec 1 [cited 2023 Jul 8];15(4):393–411. Available from: https://link.springer.com/article/10.1007/s12193-020-00348-6

40. Halabi O, Salahuddin T, Karkar AG, Alinier G. Virtual reality for ambulance simulation environment. Multimed Tools Appl [Internet]. 2022 Sep 1 [cited 2023 Jul 8];81(22):32119–37. Available from: https://link.springer.com/article/10.1007/s11042-022-12980-3

41. Schuuring MJ, Išgum I, Cosyns B, Chamuleau SAJ, Bouma BJ. Routine Echocardiography and Artificial Intelligence Solutions. Front Cardiovasc Med. 2021;8:648877.

42. Humm G, Harries RL, Stoyanov D, Lovat LB. Supporting laparoscopic general surgery training with digital technology: The United Kingdom and Ireland paradigm. BMC Surg. 2021 Mar;21(1):123.

43. Reis G, Yilmaz M, Rambach J, Pagani A, Suarez-Ibarrola R, Miernik A, et al. Mixed reality applications in urology: Requirements and future potential. Ann Med Surg (Lond). 2021 Jun;66:102394.

44. Sood J. Advancing frontiers in anaesthesiology with laparoscopy. World J Gastroenterol. 2014 Oct;20(39):14308–14.

45. Tsai SH, Liu CA, Huang KH, Lan YT, Chen MH, Chao Y, et al. Advances in Laparoscopic and Robotic Gastrectomy for Gastric Cancer. Pathol Oncol Res. 2017 Jan;23(1):13–7.

46. Tătaru OS, Vartolomei MD, Rassweiler JJ, Virgil O, Lucarelli G, Porpiglia F, et al. Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel). 2021 Feb;11(2).

47. Guimarães B, Dourado L, Tsisar S, Diniz JM, Madeira MD, Ferreira MA. Rethinking Anatomy: How to Overcome Challenges of Medical Education’s Evolution. Acta Med Port. 2017 Feb;30(2):134–40.

48. Finocchiaro M, Cortegoso Valdivia P, Hernansanz A, Marino N, Amram D, Casals A, et al. Training Simulators for Gastrointestinal Endoscopy: Current and Future Perspectives. Cancers (Basel). 2021 Mar;13(6).

49. Al-Azri NH. How to think like an emergency care provider: A conceptual mental model for decision making in emergency care. Int J Emerg Med [Internet]. 2020 Apr 16 [cited 2023 Jul 10];13(1):1–9. Available from: https://intjem.biomedcentral.com/articles/10.1186/s12245-020-00274-0

50. Zavala AM, Day GE, Plummer D, Bamford-Wade A. Decision-making under pressure: medical errors in uncertain and dynamic environments. Aust Health Rev [Internet]. 2018 [cited 2023 Jul 10];42(4):395–402. Available from: https://pubmed.ncbi.nlm.nih.gov/28578757/

51. Diller GP, Arvanitaki A, Opotowsky AR, Jenkins K, Moons P, Kempny A, et al. Lifespan Perspective on Congenital Heart Disease Research: JACC State-of-the-Art Review. J Am Coll Cardiol. 2021 May;77(17):2219–35.

52. Kristensen SE, Mosgaard BJ, Rosendahl M, Dalsgaard T, Bjørn SF, Frøding LP, et al. Robot-assisted surgery in gynecological oncology: current status and controversies on patient benefits, cost and surgeon conditions - a systematic review. Acta Obstet Gynecol Scand. 2017 Mar;96(3):274–85.

53. Taylor AT, Garcia E V. Computer-assisted diagnosis in renal nuclear medicine: rationale, methodology, and interpretative criteria for diuretic renography. Semin Nucl Med. 2014 Mar;44(2):146–58.

54. Gunasekeran DV, Tseng RMWW, Tham YC, Wong TY. Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. NPJ Digit Med. 2021 Feb;4(1):40.

55. Su Z, McDonnell D, Ahmad J, Cheshmehzangi A. Disaster preparedness in healthcare professionals amid COVID-19 and beyond: A systematic review of randomized controlled trials. Nurse Educ Pract. 2023 May 1;69:103583.

56. Mouta A, Pinto-Llorente AM, Torrecilla-Sánchez EM. Uncovering Blind Spots in Education Ethics: Insights from a Systematic Literature Review on Artificial Intelligence in Education. Int J Artif Intell Educ [Internet]. 2023; Available from: https://doi.org/10.1007/s40593-023-00384-9

57. Iniesta R. The human role to guarantee an ethical AI in healthcare: a five-facts approach. AI and Ethics [Internet]. 2023; Available from: https://doi.org/10.1007/s43681-023-00353-x

58. Otero-Varela L, Cintora AM, Espinosa S, Redondo M, Uzuriaga M, González M, et al. Extended reality as a training method for medical first responders in mass casualty incidents: A protocol for a systematic review. PLoS One [Internet]. 2023 Mar 1 [cited 2023 Jul 7];18(3):e0282698. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282698

59. Mohan D, Elmer J, Arnold RM, Forsythe RM, Fischhoff B, Rak K, et al. Testing the feasibility, acceptability, and preliminary effect of a novel deliberate practice intervention to reduce diagnostic error in trauma triage: a study protocol for a randomized pilot trial. Pilot Feasibility Stud [Internet]. 2022 Dec 1 [cited 2023 Jul 7];8(1):1–11. Available from: https://pilotfeasibilitystudies.biomedcentral.com/articles/10.1186/s40814-022-01212-y

60. Lima DS, De-Vasc Oncelos IF, Queiroz EF, Cunha TA, Dos-Santos VS, Arruda FAEL, et al. Multiple victims incident simulation: Training professionals and university teaching. Rev Col Bras Cir. 2019;46(3).

61. Raper JD, Khoury C, Bloom AD. Simulation in emergency medicine graduate medical education: a call to lead. Clin Exp Emerg Med [Internet]. 2023 Jan 30 [cited 2023 Jul 7];10(1):107–9. Available from: http://ceemjournal.org/journal/view.php?doi=10.15441/ceem.22.413

62. Park SK, Kim HJ. Development and Evaluation of Virtual Reality-based Simulation Content for Nursing Students Regarding Emergency Triage. Journal of Korean Academy of Fundamentals of Nursing [Internet]. 2023 May 31 [cited 2023 Jul 7];30(2):292–301. Available from: http://j.kafn.or.kr/journal/view.php?doi=10.7739/jkafn.2022.30.2.292

63. Arthur T, Loveland-Perkins T, Williams C, Harris D, Wilson M, de Burgh T, et al. Examining the validity and fidelity of a virtual reality simulator for basic life support training. BMC Digital Health 2023 1:1 [Internet]. 2023 May 11 [cited 2023 Jul 8];1(1):1–14. Available from: https://link.springer.com/articles/10.1186/s44247-023-00016-1

64. Aiello S, Cochrane T, Sevigny C. The affordances of clinical simulation immersive technology within healthcare education: a scoping review. Virtual Real [Internet]. 2023 Jan 14 [cited 2023 Jul 8];1:1–19. Available from: https://link.springer.com/article/10.1007/s10055-022-00745-0

65. Joshi A, Abdelsattar J, Castro-Varela A, Chase ·, Wehrle J, Cullen · Christian, et al. Incorporating mass casualty incidents training in surgical education program. Global Surgical Education - Journal of the Association for Surgical Education 2022 1:1 [Internet]. 2022 Apr 14 [cited 2023 Jul 8];1(1):1–5. Available from: https://link.springer.com/article/10.1007/s44186-022-00018-z

66. Mooney SJ, Pejaver V. Big Data in Public Health: Terminology, Machine Learning, and Privacy. Annu Rev Public Health. 2018 Apr;39:95–112.

67. Demaidi MN. Artificial intelligence national strategy in a developing country. AI Soc [Internet]. 2023; Available from: https://doi.org/10.1007/s00146-023-01779-x

68. Persson J. Artificial Intelligence and UK Education: Research, the Redistribution of Authority, and Rights. Int J Artif Intell Educ [Internet]. 2023; Available from: https://doi.org/10.1007/s40593-023-00347-0

69. Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021 Feb;22(1):14.

70. Oyelere SS, Bouali N, Kaliisa R, Obaido G, Yunusa AA, Jimoh ER. Exploring the trends of educational virtual reality games: a systematic review of empirical studies. Smart Learning Environments [Internet]. 2020 Dec 1 [cited 2023 Jul 8];7(1):1–22. Available from: https://link.springer.com/articles/10.1186/s40561-020-00142-7

71. Rodríguez-López B, Rueda J. Artificial moral experts: asking for ethical advice to artificial intelligent assistants. AI and Ethics [Internet]. 2023;3(4):1371–9. Available from: https://doi.org/10.1007/s43681-022-00246-5

72. Mouchabac S, Adrien V, Falala-Séchet C, Bonnot O, Maatoug R, Millet B, et al. Psychiatric Advance Directives and Artificial Intelligence: A Conceptual Framework for Theoretical and Ethical Principles. Front Psychiatry. 2020;11:622506.

73. Savoia E, Lin L, Bernard D, Klein N, James LP, Guicciardi S. Public Health System Research in Public Health Emergency Preparedness in the United States (2009-2015): Actionable Knowledge Base. Am J Public Health. 2017 Sep;107(S2):e1–6.

74. Bharat C, Hickman M, Barbieri S, Degenhardt L. Big data and predictive modelling for the opioid crisis: existing research and future potential. Lancet Digit Health. 2021 Jun;3(6):e397–407.

75. Platts-Mills TF, Nagurney JM, Melnick ER. Tolerance of Uncertainty and the Practice of Emergency Medicine. Ann Emerg Med [Internet]. 2020 Jun 1 [cited 2023 Jul 10];75(6):715–20. Available from: http://www.annemergmed.com/article/S0196064419313174/fulltext

Downloads

Published

2024-01-01

How to Cite

1.
Zavala Calahorrano AM, Del Pozo Sánchez G, Chaves Corral KN. Systematic review MedTech, and Artificial Intelligence. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Jan. 1 [cited 2024 Dec. 12];3:789. Available from: https://conferencias.ageditor.ar/index.php/sctconf/article/view/975