Innovations in medicine: modern challenges, future definitions: A narrative review
DOI:
https://doi.org/10.57125/FEM.2022.06.30.03Keywords:
medical innovations, artificial intelligence, challenges, and healthcareAbstract
Background The improvement of healthcare services is one of the highest priorities for all societies worldwide. Healthcare services have shown vivid changes in the last decades due to technological advances, from examining patients and diagnosis to recently developed drugs. Although there are significant efforts to improve healthcare, humanity faces many challenges.
Aim: to study the recent medical innovations in the different aspects of healthcare services. The authors also aim to investigate the various modern challenges in medicine.
Methods: PubMed, Web of Science, Scopus, Embase, SpringerLink, and the Cochrane Library were searched in order to find various articles concerning this topic. The next search strategies were used: (medical innovations) AND (artificial intelligence) AND (medical challenges). These strategies were used for searches that ended in April 2022.
Scientific novelty: Doctors used to make their own opinions in varied cases by means of limited tools. However, together with the introduction of modern technologies into the healthcare system over the previous few decades, medical practice has become increasingly dependent on technology. Medical technology is the development of instruments with the overall purpose of increasing patients' quality of life. There is much anticipation for the new tools one can create and for the outcomes they will enable, as diagnostic and treatment equipment constantly improves. Therefore, in this review, the aim is to address the tactics of the new technologies and applications in medicine.
Conclusion: Medical innovations exist in all healthcare aspects, from education and diagnosis to management. Recent technologies have demonstrated a considerable benefit in healthcare services improvement. However, a number of obstacles that both societies and individuals face are illustrated. There is a growing interest in resolving current concerns and challenges and obtaining the greatest benefits from medical breakthroughs.
References
Flessa S, Huebner C. Innovations in health care—A conceptual framework. Int J Environ Res Public Health [Internet]. 2021;18(19):10026. Available from: http://dx.doi.org/10.3390/ijerph181910026
Belmonte EM, Tortosa SO, Ortega L de-Marcos, Gutiérrez-Martínez J-M. Healthcare information technology: A systematic mapping study. Healthc Inform Res [Internet]. 2023;29(1):4–15. Available from: http://dx.doi.org/10.4258/hir.2023.29.1.4
Mitchell M, Kan L. Digital technology and the future of health systems. Health Syst Reform [Internet]. 2019;5(2):113–20. Available from: http://dx.doi.org/10.1080/23288604.2019.1583040
Sayani S, Muzammil M, Saleh K, Muqeet A, Zaidi F, Shaikh T. Addressing cost and time barriers in chronic disease management through telemedicine: an exploratory research in select low- and middle-income countries. Ther Adv Chronic Dis [Internet]. 2019;10:2040622319891587. Available from: http://dx.doi.org/10.1177/2040622319891587
Sandberg CEJ, Knight SR, Qureshi AU, Pathak S. Using telemedicine to diagnose surgical site infections in low- and middle-income countries: Systematic review. JMIR MHealth UHealth [Internet]. 2019;7(8):e13309. Available from: http://dx.doi.org/10.2196/13309
Fleßa S, Aichinger H, Bratan T. Innovationsmanagement diagnostischer Geräte am Beispiel der Detektion zirkulierender Tumorzellen. TumorDiagn Ther [Internet]. 2021;42(05):378–87. Available from: http://dx.doi.org/10.1055/a-1466-9490
Coates RS, Mofidi R. Financial sustainability of NHS Foundation Trusts in England five years after the enactment of the Health and Social Care Act of 2012. Br J Health Care Manag [Internet]. 2019;25(6):1–9. Available from: http://dx.doi.org/10.12968/bjhc.2019.0017
Kelly CJ, Young AJ. Promoting innovation in healthcare. Future Healthc J [Internet]. 2017;4(2):121–5. Available from: http://dx.doi.org/10.7861/futurehosp.4-2-121
Kirch DG, Petelle K. Addressing the physician shortage: The peril of ignoring demography. JAMA [Internet]. 2017;317(19):1947. Available from: http://dx.doi.org/10.1001/jama.2017.2714
Miller DD, Brown EW. Artificial intelligence in medical practice: The question to the answer? Am J Med [Internet]. 2018;131(2):129–33. Available from: http://dx.doi.org/10.1016/j.amjmed.2017.10.035
Bosque Ortiz G, Hsiang W. Medical Technology. The Yale Journal of Biology and Medicine, 01 Sep 2018, 91(3):203-205
Mesko B. Health IT and digital health: The future of health technology is diverse. J Clin Transl Res. 2018 Sep 8;3(Suppl 3):431-434. PMID: 30873492; PMCID: PMC6412600.
Laal M. Innovation process in medical imaging. Procedia Soc Behav Sci [Internet]. 2013;81:60–4. Available from: http://dx.doi.org/10.1016/j.sbspro.2013.06.388
Gore JC. Artificial intelligence in medical imaging. Magn Reson Imaging. 2020 May;68:A1-A4. doi: 10.1016/j.mri.2019.12.006
Roobottom CA, Mitchell G, Morgan-Hughes G. Radiation-reduction strategies in cardiac computed tomographic angiography. Clin Radiol [Internet]. 2010;65(11):859–67. Available from: http://dx.doi.org/10.1016/j.crad.2010.04.021
Kaur M, Wasson V. ROI based medical image compression for telemedicine application. Procedia Comput Sci [Internet]. 2015;70:579–85. Available from: http://dx.doi.org/10.1016/j.procs.2015.10.037
Hussain S, Mubeen I, Ullah N, Shah SSUD, Khan BA, Zahoor M, et al. Modern diagnostic imaging technique applications and risk factors in the medical field: A review. Biomed Res Int [Internet]. 2022;2022:5164970. Available from: http://dx.doi.org/10.1155/2022/5164970
Rehman A, Ma S. Waraich Mm. CT Scan. Prof Med J. 2023;16(04):579–82.
Paulo G, Damilakis J, Tsapaki V, Schegerer AA, Repussard J, Jaschke W, et al. Diagnostic Reference Levels based on clinical indications in computed tomography: a literature review. Insights Imaging [Internet]. 2020;11(1):96. Available from: http://dx.doi.org/10.1186/s13244-020-00899-y
Meulepas JM, Ronckers CM, Smets AMJB, Nievelstein RAJ, Jahnen A, Lee C, et al. Leukemia and brain tumors among children after radiation exposure from CT scans: design and methodological opportunities of the Dutch Pediatric CT Study. Eur J Epidemiol. 2014 Apr;29(4):293–301.
Edelman RR. The history of MR imaging as seen through the pages of radiology. Radiology [Internet]. 2014;273(2 Suppl):S181-200. Available from: http://dx.doi.org/10.1148/radiol.14140706
Lee K, Park HY, Kim KW, Lee AJ, Yoon MA, Chae EJ, et al. Advances in whole body MRI for musculoskeletal imaging: Diffusion-weighted imaging. J Clin Orthop Trauma [Internet]. 2019;10(4):680–6. Available from: http://dx.doi.org/10.1016/j.jcot.2019.05.018
Jungmann PM, Agten CA, Pfirrmann CW, Sutter R. Advances in MRI around metal: MRI around metal. J Magn Reson Imaging [Internet]. 2017;46(4):972–91. Available from: http://dx.doi.org/10.1002/jmri.25708
van Beek EJR, Kuhl C, Anzai Y, Desmond P, Ehman RL, Gong Q, et al. Value of MRI in medicine: More than just another test? J Magn Reson Imaging [Internet]. 2019;49(7):e14–25. Available from: http://dx.doi.org/10.1002/jmri.26211
Seo J, Kim Y-S. Ultrasound imaging and beyond: recent advances in medical ultrasound. Biomed Eng Lett [Internet]. 2017;7(2):57–8. Available from: http://dx.doi.org/10.1007/s13534-017-0030-7
Miao JH, H. K. Cardiotocographic diagnosis of fetal health based on multiclass morphologic pattern predictions using deep learning classification. Int J Adv Comput Sci Appl [Internet]. 2018;9(5). Available from: http://dx.doi.org/10.14569/ijacsa.2018.090501
Lanza GM. Ultrasound imaging: Something old or something new? Invest Radiol [Internet]. 2020;55(9):573–7. Available from: http://dx.doi.org/10.1097/rli.0000000000000679
Combi C, Pozzani G, Pozzi G. Telemedicine for developing countries: A survey and some design issues. Appl Clin Inform [Internet]. 2016;07(04):1025–50. Available from: http://dx.doi.org/10.4338/aci-2016-06-r-0089
Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial Intelligence in Healthcare. Elsevier; 2020. p. 25–60.
paulhoulihan. Johnson & Johnson aims to make VR training available to every surgeon [Internet]. vStream Digital Media. vStream; 2019 [cited 2022 Apr 27]. Available from: https://vstream.ie/johnson-johnson-vr-training-surgery/
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer [Internet]. 2018;18(8):500–10. Available from: http://dx.doi.org/10.1038/s41568-018-0016-5
Nabavi S, Mohammadi M. Book review: Artificial Intelligence in Medical Imaging, Opportunities, Applications and Risks edited by Erik R. Ranschaert, Sergey Morozov and Paul R. Algra: Springer Nature Switzerland AG, 2019, 373 p.
Oren O, Gersh BJ, Bhatt DL. Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit Health [Internet]. 2020;2(9):e486–8. Available from: http://dx.doi.org/10.1016/S2589-7500(20)30160-6
Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol [Internet]. 2018;15(2):81–94. Available from: http://dx.doi.org/10.1038/nrclinonc.2017.166
Martinelli C, Pucci C, Ciofani G. Nanostructured carriers as innovative tools for cancer diagnosis and therapy. APL Bioeng [Internet]. 2019;3(1):011502. Available from: http://dx.doi.org/10.1063/1.5079943
Bayda S, Hadla M, Palazzolo S, Riello P, Corona G, Toffoli G, et al. Inorganic nanoparticles for cancer therapy: A transition from lab to clinic. Curr Med Chem [Internet]. 2018;25(34):4269–303. Available from: http://dx.doi.org/10.2174/0929867325666171229141156
Bazak R, Houri M, El Achy S, Kamel S, Refaat T. Cancer active targeting by nanoparticles: a comprehensive review of literature. J Cancer Res Clin Oncol [Internet]. 2015;141(5):769–84. Available from: http://dx.doi.org/10.1007/s00432-014-1767-3
Yu K-H, Zhang C, Berry GJ, Altman RB, Ré C, Rubin DL, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun [Internet]. 2016;7(1):12474. Available from: http://dx.doi.org/10.1038/ncomms12474
Aerts HJWL. The potential of radiomic-based phenotyping in precision medicine: A review: A review. JAMA Oncol [Internet]. 2016;2(12):1636–42. Available from: http://dx.doi.org/10.1001/jamaoncol.2016.2631
Wang R, Dai W, Gong J, Huang M, Hu T, Li H, et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol [Internet]. 2022;15(1):11. Available from: http://dx.doi.org/10.1186/s13045-022-01225-3
Volosovets OP, Lurin IA, Naumenko OM, Volosovets AO, Kryvopustov SP. Current challenges for the Health care system due to the lack of medical staff and the continuous professional development of doctors. Wiad Lek [Internet]. 2022;75(5 pt 1):1136–9. Available from: http://dx.doi.org/10.36740/WLek202205115
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 authors

This work is licensed under a Creative Commons Attribution 4.0 International License.
