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Healthcare Systems and Artificial Intelligence: Focus on Challenges and the International Regulatory Framework

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Abstract

Background

Nowadays, healthcare systems are coping with the challenge of countering the exponential growth of healthcare costs worldwide, to support sustainability and to guarantee access to treatment for all patients.

Methods

Artificial Intelligence (AI) is the technology able to perform human cognitive functions through the creation of algorithms. The value of AI in healthcare and its ability to address healthcare delivery issues has been a subject of discussion within the scientific community for several years.

Results

The aim of this work is to provide an overview of the primary uses of AI in the healthcare system, to discuss its desirable future uses while shedding light on the major issues related to implications within international regulatory processes. In this manuscript, it will be described the main applications of AI in various aspects of health care, from clinical studies to ethical implications, focusing on the international regulatory framework in countries in which AI is used, to discuss and compare strengthens and weaknesses.

Conclusions

The challenges in regulatory processes to facilitate the integration of AI in healthcare are significant. However, overcoming them is essential to ensure that AI-based technologies are adopted safely and effectively.

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Data Availability

Full availability of data and materials. All stated data can be provided on request to the reader.

Code Availability

Not applicable.

References

  1. Snowdon A. Digital health: a framework for healthcare transformation. 2020. Available online: https://www.gs1ca.org/documents/digital_health-affht.pdf. Accessed 23 Jan 2023.

  2. Williams OD. COVID-19 and private health: market and governance failure. Development (Rome). 2020;63(2–4):181–90. https://doi.org/10.1057/s41301-020-00273-x.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Desai AN. Artificial intelligence: promise, pitfalls, and perspective. JAMA. 2020;323(24):2448–9. https://doi.org/10.1001/jama.2020.8737.

    Article  PubMed  Google Scholar 

  4. Reddy S, Fox J, Purohit MP. Artificial intelligence-enabled healthcare delivery. J R Soc Med. 2019;112(1):22–8. https://doi.org/10.1177/0141076818815510.

    Article  PubMed  Google Scholar 

  5. Gampala S, Vankeshwaram V, Gadula SSP. Is artificial intelligence the new friend for radiologists? a review article. Cureus. 2020;12(10):e11137. https://doi.org/10.7759/cureus.11137.

  6. Renn BN, Schurr M, Zaslavsky O, Pratap A. Artificial intelligence: an interprofessional perspective on implications for geriatric mental health research and care. Front Psychiatry. 2021;12:734909. https://doi.org/10.3389/fpsyt.2021.734909.

  7. Oduoye MO, Fatima E, Muzammil MA, Dave T, Irfan H, Fariha FNU, Marbell A, Ubechu SC, Scott GY, Elebesunu EE. Impacts of the advancement in artificial intelligence on laboratory medicine in low- and middle-income countries: challenges and recommendations-A literature review. Health Sci Rep. 2024;7(1): e1794. https://doi.org/10.1002/hsr2.1794.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Yang J, Huang J, Han D, Ma X. Artificial intelligence applications in the treatment of colorectal cancer: a narrative review. Clin Med Insights Oncol. 2024;5(18):11795549231220320. https://doi.org/10.1177/11795549231220320.

    Article  Google Scholar 

  9. Cai Y, Zhang X, Cao J, Grzybowski A, Ye J, Lou L. Application of artificial intelligence in oculoplastics: a review. Clin Dermatol. 2024:S0738-081X(23)00271-7. https://doi.org/10.1016/j.clindermatol.2023.12.019.

  10. Rana HS, Umer M, Hassan U, Asgher U. A novel multi-criteria decision-making approach for prioritization of elective surgeries through formulation of “weighted MeNTS scoring system.” Heliyon. 2022;8(8). https://doi.org/10.1016/j.heliyon.2022.e10339.

  11. Gebremichael LG, Champion S, Nesbitt K, Pearson V, Bulamu NB, Dafny HA, Sajeev S, Pinero de Plaza MA, Ramos JS, Suebkinorn O, Gulyani A, Bulto LN, Beleigoli A, Hendriks JM, Hines S, Clark RA; NHMRC CHAP Project Team. Effectiveness of cardiac rehabilitation programs on medication adherence in patients with cardiovascular disease: a systematic review and meta-analysis. Int J Cardiol Cardiovasc Risk Prev. 2023;20:200229. https://doi.org/10.1016/j.ijcrp.2023.200229.

  12. Marchiò C, Criscitiello C, Scatena C, Santinelli A, Graziano P, Malapelle U, Cursano G, Venetis K, Fanelli GN, Pepe F, Berrino E, Angelis C, Perrone G, Curigliano G, Fusco N. Think, “HER2” different: integrative diagnostic approaches for HER2-low breast cancer. Pathologica. 2023;115(6):292–301. https://doi.org/10.32074/1591-951X-942.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Feng J, Phillips RV, Malenica I, Bishara A, Hubbard AE, Celi LA, et al. Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. NPJ Digit Med. 2022;5(1):66. https://doi.org/10.1038/s41746-022-00611-y.

  14. Park Y, Jackson GP, Foreman MA, Gruen D, Hu J, Das AK. Evaluating artificial intelligence in medicine: phases of clinical research. JAMIA Open. 2020;3(3):326–31. https://doi.org/10.1093/jamiaopen/ooaa033.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kim HE, Kim HH, Han BK, Kim KH, Han K, Nam H, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health. 2020;2(3):e138–48. https://doi.org/10.1016/S2589-7500(20)30003-0.

    Article  PubMed  Google Scholar 

  16. Koçak B, Cuocolo R, dos Santos DP, Stanzione A, Ugga L. Must-have qualities of clinical research on artificial intelligence and machine learning. Balkan Med J. 2023;40(1):3–12. https://doi.org/10.4274/balkanmedj.galenos.2022.2022-11-51.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wu H, Chan NK, Zhang CJP, Ming WK. The role of the sharing economy and artificial intelligence in health care: opportunities and challenges. J Med Internet Res. 2019;21(10). https://www.jmir.org/2019/10/e13469.

  18. Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med. 2023;183(6):589–96. https://doi.org/10.1001/jamainternmed.2023.1838.

    Article  PubMed  Google Scholar 

  19. Zhang X, Xiao C, Glass LM, Sun J. DeepEnroll: patient-trial matching with deep embedding and entailment prediction. In: The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020; 2020.

  20. Dikshit A, Pradhan B. Interpretable and explainable AI (XAI) model for spatial drought prediction. Sci Total Environ. 2021;801:149797. https://doi.org/10.1016/j.scitotenv.2021.149797.

  21. Walsh CA, Cahir C, Tecklenborg S, Byrne C, Culbertson MA, Bennett KE. The association between medication non-adherence and adverse health outcomes in ageing populations: a systematic review and meta-analysis. Br J Clin Pharmacol. 2019;85(11):2464–78. https://doi.org/10.1111/bcp.14075.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res. 2023;93(2):334–41. https://doi.org/10.1038/s41390-022-02226-1.

    Article  PubMed  Google Scholar 

  23. Bernstein P. What is Artificial Intelligence (AI)? In: Machine Learning. 2022. https://www.taylorfrancis.com/chapters/mono/10.4324/9781003297192-3/artificial-intelligence-ai-phil-bernstein.

  24. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–58. https://doi.org/10.1056/NEJMra1814259.

    Article  PubMed  Google Scholar 

  25. Lee SI, Celik S, Logsdon BA, Lundberg SM, Martins TJ, Oehler VG, et al. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nat Commun. 2018;9(1):42. https://doi.org/10.1038/s41467-017-02465-5.

  26. World Bank Group. Distributed Ledger Technology (DLT) and Blockchain Acknowledgments III. World Bank. 2017;(1). https://documents.worldbank.org/en/publication/documents-reports/documentdetail/177911513714062215/distributed-ledger-technology-dlt-and-blockchain.

  27. Alruwaili FF. Artificial intelligence and multi agent based distributed ledger system for better privacy and security of electronic healthcare records. PeerJ Comput Sci. 2020;6:e323. https://doi.org/10.7717/peerj-cs.323.

  28. IMDRF (International Medical Device Regulators Forum, IMDRF/AIMD WG/N67, 9 May 2022. https://ipi.academy/product/industry/25/medical-devices?utm_source=bing&utm_medium=cpc&utm_campaign=wrd-mads-medical-device&msclkid=4822df0402af10fce086bae707daac28.

  29. Le Glaz A, Haralambous Y, Kim-Dufor DH, Lenca P, Billot R, Ryan TC, et al. Machine learning and natural language processing in mental health: systematic review. J Med Internet Res. 2021;23(5):e15708. https://doi.org/10.2196/15708.

  30. Wren JD. The emerging in-silico scientist: how text-based bioinformatics is bridging biology and artificial intelligence. IEEE Eng Med Biol Mag. 2004;23(2):87–93. https://doi.org/10.1109/memb.2004.1310989.

    Article  PubMed  Google Scholar 

  31. Altman RB. Artificial intelligence (AI) systems for interpreting complex medical datasets. Clin Pharmacol Ther. 2017;101(5):585–6. https://doi.org/10.1002/cpt.650.

    Article  CAS  PubMed  Google Scholar 

  32. Trunfio M, Rossi S. Advances in metaverse investigation: streams of research and future agenda. Virtual Worlds. 2022;1(2). https://www.mdpi.com/2813-2084/1/2/7.

  33. Yeung AWK, Tosevska A, Klager E, Eibensteiner F, Laxar D, Stoyanov J, et al. Virtual and augmented reality applications in medicine: analysis of the scientific literature. J Med Internet Res. 2021;23(2):e25499. https://doi.org/10.2196/25499.

  34. Thorp HH. ChatGPT is fun, but not an author. Science. 2023;379. https://www.science.org/doi/10.1126/science.adg7879.

  35. Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel). 2023;11(6):887. https://doi.org/10.3390/healthcare11060887.

  36. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577(7792):706–10. https://doi.org/10.1038/s41586-019-1923-7.

    Article  CAS  PubMed  Google Scholar 

  37. Madhukar NS, Khade PK, Huang L, Gayvert K, Galletti G, Stogniew M, et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat Commun. 2019;10(1). https://api.semanticscholar.org/CorpusID:208145918.

  38. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20(2):273–86. https://doi.org/10.1093/biostatistics/kxx069.

    Article  PubMed  Google Scholar 

  39. Glicksberg BS, Miotto R, Johnson KW, Shameer K, Li L, Chen R, et al. Automated disease cohort selection using word embeddings from Electronic Health Records. Pac Symp Biocomput. 2018;23:145–56. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5788312/pdf/nihms921845.pdf.

  40. Liao KP, Cai T, Savova GK, Murphy SN, Karlson EW, Ananthakrishnan AN, et al. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ. 2015;350:h1885. https://doi.org/10.1136/bmj.h1885.

  41. Bain EE, Shafner L, Walling DP, Othman AA, Chuang-Stein C, Hinkle J, et al. Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia. JMIR Mhealth Uhealth. 2017;5(2). https://mhealth.jmir.org/2017/2/e18.

  42. Gavrielov-Yusim N, Kürzinger ML, Nishikawa C, Pan C, Pouget J, Epstein LB, et al. Comparison of text processing methods in social media-based signal detection. Pharmacoepidemiol Drug Saf. 2019;28(10):1309–17. https://doi.org/10.1002/pds.4857.

    Article  PubMed  Google Scholar 

  43. Cascini F, Beccia F, Causio FA, Melnyk A, Zaino A, Ricciardi W. Scoping review of the current landscape of AI-based applications in clinical trials. Front Public Health. 2022;10. https://publires.unicatt.it/it/publications/scoping-review-of-the-current-landscape-of-ai-based-applications.

  44. World Health Organisation. WHO Issues First Global Report on Artificial Intelligence (AI) in Health and Six Guiding Principles for Its Design and Use. 2021. Available online: https://www.who.int/news/item/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use. Accessed 15 Dec 2021.

  45. Beard RW. Decision making under uncertainty: theory and application [Bookshelf]. IEEE Control Syst. 2018;38(6). https://ieeexplore.ieee.org/document/8536567.

  46. U.S. Food and Drug Administration. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD). https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device. Accessed 22 Sept 2021.

  47. Overview of medical device classification and reclassification. Available at: https://www.fda.gov/about-fda/cdrh-transparency/overview-medical-device-classification-and-reclassification. Acccessed 25 July 2019.

  48. U.S. Food and Drug Administration. Software as a Medical Device (SaMD). http://www.fda.gov/medical-devices/digital-health/software-medical-device-samd. Accessed 2 July 2019.

  49. Wang T. Outpatient clinic process enhancement at children’s hospital: empoweredby information technology. Chin J Hosp Adm. 2018;34:753–5. https://api.semanticscholar.org/CorpusID:224250149.

  50. Vokinger KN, Mühlematter UJ, Becker A, Boss A, Reutter MA, Szucs TD. Artificial intelligence und machine learning in der medizin. Aug 28, 2017. https://jusletter.weblaw.ch/juslissues/2017/903/artificial-intellige_da49225588.html__ONCE&login=false. Accessed 26 Dec 2020.

  51. Swissmedic. Guide to the regulation of medical devices. Nov 6, 2017. https://www.swissmedic.ch/swissmedic/en/home/medical-devices/regulation-of-medical-devices/medical-device-regulation_onlineguide.html. Accessed 1 Oct 2020.

  52. 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;9(5):745–53. https://doi.org/10.1007/s13244-018-0645-y.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Romagnoli A, Zovi A, Sabbatucci M, Ferrara F, Vitiello A. Therapeutic innovation and digital therapies in the world: Comparison between the American and European regulatory framework, with focus on Italy. J Interprof Educ Pract. 2023;32. https://www.sciencedirect.com/science/article/abs/pii/S2405452623000587.

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Funding

No funding was received to conduct this study.

Author information

Authors and Affiliations

  1. Territorial Pharmaceutical Service, Local Health Unit Lanciano Vasto Chieti, Chieti, Italy

    Alessia Romagnoli

  2. Pharmaceutical Department, Asl Napoli 3 Sud, Dell’amicizia street 22, 80035, Nola, Naples, Italy

    Francesco Ferrara

  3. Italian Society of Hospital Pharmacy (SIFO), SIFO Secretariat of the Lombardy Region, Carlo Farini street, 81, 20159, Milan, Italy

    Roberto Langella

  4. Ministry of Health, Viale Giorgio Ribotta 5, 00144, Rome, Italy

    Andrea Zovi

Authors
  1. Alessia Romagnoli
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  2. Francesco Ferrara
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  3. Roberto Langella
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  4. Andrea Zovi
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Contributions

AR: Conceptualization, Writing—original draft, Methodology

FF: Supervision, Validation

RL: Writing—review & editing, Supervision, Validation

AZ: Writing—review & editing, Supervision, Validation

Corresponding author

Correspondence to Francesco Ferrara.

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Romagnoli, A., Ferrara, F., Langella, R. et al. Healthcare Systems and Artificial Intelligence: Focus on Challenges and the International Regulatory Framework. Pharm Res 41, 721–730 (2024). https://doi.org/10.1007/s11095-024-03685-3

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