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Ethical and regulatory challenges of AI technologies in healthcare: A narrative review

Mennella, Ciro, Maniscalco, Umberto, De Pietro, Giuseppe, Esposito, Massimo·2024·Heliyon
DOI 10.1016/j.heliyon.2024.e26297PMC PMC10879008
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  • Governments are increasingly leveraging biometric data beyond public health, including in national security, immigration, and criminal justice contexts.

    “Governments are increasingly leveraging biometric data beyond public health, including in national security, immigration, and criminal justice contexts 67,68 .”

    From Privacy in consumer wearable technologies: a living systematic analysis of data policies across leading manufacturers (2025) · cites this paper as [67]

    The Foundations section describes growing biosurveillance and raises concern that governments or companies may reuse biometric data for non-health purposes, including criminal justice and immigration. It does not mention national security or establish that governments are increasingly using the data in those contexts; it frames such reuse as a concern. (LLM, conf=0.88)

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  1. [1]W.H. Organization Integrated care for older people: guidelines on community-level interventions to manage declines in intrinsic capacity 2017 29608259
  2. [3]Magrabi F. Ammenwerth E. McNair J.B. De Keizer N.F. Hyppönen H. Nykänen P. Rigby M. Scott P.J. Vehko T. Wong Z.S.-Y. Artificial intelligence in clinical decision support: challenges for evaluating ai and practical implications Yearb. Med. Inform. 28 2019 128 134 31022752 PMC6697499
  3. [5]Srinivasu P.N. SivaSai J.G. Ijaz M.F. Bhoi A.K. Kim W. Kang J.J. Classification of skin disease using deep learning neural networks with mobilenet v2 and lstm Sensors 21 2021 2852 33919583 PMC8074091
  4. [6]Vulli A. Srinivasu P.N. Sashank M.S.K. Shafi J. Choi J. Ijaz M.F. Fine-tuned densenet-169 for breast cancer metastasis prediction using fastai and 1-cycle policy Sensors 22 2022 2988 35458972 PMC9025766
  5. [8]Ahmad I. Merla A. Ali F. Shah B. AlZubi A.A. AlZubi M.A. A deep transfer learning approach for Covid-19 detection and exploring a sense of belonging with diabetes Front. Public Health 11 2023 PMC10657998 38026271
  6. [9]Praveen S.P. Srinivasu P.N. Shafi J. Wozniak M. Ijaz M.F. Resnet-32 and fastai for diagnoses of ductal carcinoma from 2d tissue slides Sci. Rep. 12 2022 20804 PMC9716161 36460697
  7. [10]Kumar M. Verma S. Kumar A. Ijaz M.F. Rawat D.B. Anaf-iomt: a novel architectural framework for iomt-enabled smart healthcare system by enhancing security based on recc-vc IEEE Trans. Ind. Inform. 18 2022 8936 8943
  8. [12]Slavin R.E. Best evidence synthesis: an intelligent alternative to meta-analysis J. Clin. Epidemiol. 48 1995 9 18 7853053
  9. [15]Bengio Y. Courville A. Vincent P. Representation learning: a review and new perspectives IEEE Trans. Pattern Anal. Mach. Intell. 35 2013 1798 1828 23787338
  10. [17]Schmidhuber J. Deep learning in neural networks: an overview Neural Netw. 61 2015 85 117 25462637
  11. [21]Liu R. Wang M. Zheng T. Zhang R. Li N. Chen Z. Yan H. Shi Q. An artificial intelligence-based risk prediction model of myocardial infarction BMC Bioinform. 23 2022 1 17 PMC9175344 35672659
  12. [22]Lambercy O. Lehner R. Chua K. Wee S.K. Rajeswaran D.K. Kuah C.W.K. Ang W.T. Liang P. Campolo D. Hussain A. Neurorehabilitation from a distance: can intelligent technology support decentralized access to quality therapy? Front. Robot. AI 8 2021 612415 PMC8132098 34026855
  13. [23]Mennella C. Maniscalco U. De Pietro G. Esposito M. The role of artificial intelligence in future rehabilitation services: a systematic literature review IEEE Access 2023
  14. [24]Russell S.J. Artificial Intelligence a Modern Approach 2010 Pearson Education, Inc.
  15. [31]Kumar Y. Koul A. Singla R. Ijaz M.F. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda J. Ambient Intell. Humaniz. Comput. 2022 1 28 PMC8754556 35039756
  16. [33]Wang D. Khosla A. Gargeya R. Irshad H. Beck A.H. Deep learning for identifying metastatic breast cancer arXiv preprint arXiv:1606.05718 2016 · arXiv
  17. [35]Rajpurkar P. Irvin J. Zhu K. Yang B. Mehta H. Duan T. Ding D. Bagul A. Langlotz C. Shpanskaya K. Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning arXiv preprint arXiv:1711.05225 2017 · arXiv
  18. [36]Thomas M. Murali S. Simpson B.S.S. Freeman A. Kirkham A. Kelly D. Whitaker H.C. Zhao Y. Emberton M. Norris J.M. Use of artificial intelligence in the detection of primary prostate cancer in multiparametric mri with its clinical outcomes: a protocol for a systematic review and meta-analysis BMJ Open 13 2023 e074009 PMC10445392 37607794
  19. [37]Bedi G. Carrillo F. Cecchi G.A. Slezak D.F. Sigman M. Mota N.B. Ribeiro S. Javitt D.C. Copelli M. Corcoran C.M. Automated analysis of free speech predicts psychosis onset in high-risk youths npj Schizophr. 1 2015 1 7 PMC4849456 27336038
  20. [39]Chou C.-Y. Hsu D.-Y. Chou C.-H. Predicting the onset of diabetes with machine learning methods J. Pers. Med. 13 2023 406 36983587 PMC10057336
  21. [40]Gudigar A. Raghavendra U. Nayak S. Ooi C.P. Chan W.Y. Gangavarapu M.R. Dharmik C. Samanth J. Kadri N.A. Hasikin K. Role of artificial intelligence in Covid-19 detection Sensors 21 2021 8045 34884045 PMC8659534
  22. [41]Khanna V.V. Chadaga K. Sampathila N. Prabhu S. Chadaga R. Umakanth S. Diagnosing Covid-19 using artificial intelligence: a comprehensive review Netw. Model. Anal. Health Inform. Bioinform. 11 2022 25
  23. [42]Krishnan K.S. Krishnan K.S. Vision transformer based Covid-19 detection using chest x-rays 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC) 2021 IEEE 644 648
  24. [43]Secinaro S. Calandra D. Secinaro A. Muthurangu V. Biancone P. The role of artificial intelligence in healthcare: a structured literature review BMC Med. Inform. Decis. Mak. 21 2021 1 23 33836752 PMC8035061
  25. [44]Oren O. Gersh B.J. Bhatt D.L. Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints Lancet Digit. Health 2 2020 e486 e488 33328116
  26. [46]Lambercy O. Lehner R. Chua K. Wee S.K. Rajeswaran D. Kuah C. Ang W. Liang P. Campolo D. Hussain A. Aguirre-Ollinger G. Guan C. Kanzler C. Wenderoth N. Gassert R. Neurorehabilitation from a distance: can intelligent technology support decentralized access to quality therapy? Front. Robot. AI 8 2021 612415 PMC8132098 34026855
  27. [47]Natarajan A. Su H.-W. Heneghan C. Assessment of physiological signs associated with Covid-19 measured using wearable devices npj Digit. Med. 3 2020 156 33299095 PMC7705652
  28. [48]Al Kuwaiti A. Nazer K. Al-Reedy A. Al-Shehri S. Al-Muhanna A. Subbarayalu A.V. Al Muhanna D. Al-Muhanna F.A. A review of the role of artificial intelligence in healthcare J. Pers. Med. 13 2023 951 37373940 PMC10301994
  29. [50]Shaik T. Tao X. Higgins N. Li L. Gururajan R. Zhou X. Acharya U.R. Remote patient monitoring using artificial intelligence: current state, applications, and challenges Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 13 2023 e1485
  30. [51]Kim J. Campbell A.S. Wang J. Wearable non-invasive epidermal glucose sensors: a review Talanta 177 2018 163 170 29108571
  31. [52]Anderson D. Artificial intelligence and applications in PM&R Am. J. Phys. Med. Rehabil. 98 2019 e128 e129 30839314
  32. [53]Luxton D.D. Riek L.D. Artificial intelligence and robotics in rehabilitation https://doi.org/10.1037/0000129-031 2019 · DOI
  33. [54]Goldzweig C.L. Orshansky G. Paige N.M. Towfigh A.A. Haggstrom D.A. Miake-Lye I. Beroes J.M. Shekelle P.G. Electronic patient portals: evidence on health outcomes, satisfaction, efficiency, and attitudes: a systematic review Ann. Intern. Med. 159 2013 677 687 24247673
  34. [55]Sinsky C.A. Willard-Grace R. Schutzbank A.M. Sinsky T.A. Margolius D. Bodenheimer T. In search of joy in practice: a report of 23 high-functioning primary care practices Ann. Fam. Med. 11 2013 272 278 23690328 PMC3659145
  35. [56]Tran V.-T. Riveros C. Ravaud P. Patients' views of wearable devices and ai in healthcare: findings from the compare e-cohort npj Digit. Med. 2 53 2019 PMC6572821 31304399
  36. [57]Soliño-Fernandez D. Ding A. Bayro-Kaiser E. Ding E.L. Willingness to adopt wearable devices with behavioral and economic incentives by health insurance wellness programs: results of a US cross-sectional survey with multiple consumer health vignettes BMC Public Health 19 2019 1 8 31839006 PMC6912942
  37. [59]Chidambaram S. Maheswaran Y. Patel K. Sounderajah V. Hashimoto D.A. Seastedt K.P. McGregor A.H. Markar S.R. Darzi A. Using artificial intelligence-enhanced sensing and wearable technology in sports medicine and performance optimisation Sensors 22 2022 6920 36146263 PMC9502817
  38. [60]Gichoya J.W. McCoy L.G. Celi L.A. Ghassemi M. Equity in essence: a call for operationalising fairness in machine learning for healthcare BMJ Health Care Inf. 28 2021 PMC8733939 33910923
  39. [61]Mennella C. Maniscalco U. De Pietro G. Esposito M. A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions Comput. Biol. Med. 2023 107485 37742419
  40. [63]Javaid M. Haleem A. Singh R.P. Chatgpt for healthcare services: an emerging stage for an innovative perspective BenchCouncil Trans. Benchmarks, Stand. Eval., vol. 3 2023 100105
  41. [64]Beauchamp T.L. Childress J.F. Principles of Biomedical Ethics 2001 Oxford University Press USA
  42. [65]Guideline W. Recommendations on Digital Interventions for Health System Strengthening 2019 World Health Organization 2020-10 31162915
  43. [68]Tachkov K. Zemplenyi A. Kamusheva M. Dimitrova M. Siirtola P. Pontén J. Nemeth B. Kalo Z. Petrova G. Barriers to use artificial intelligence methodologies in health technology assessment in central and East European countries Front. Public Health 10 2022 921226 PMC9330148 35910914
  44. [69]Marcus G. Deep learning: a critical appraisal arXiv preprint arXiv:1801.00631 2018 · arXiv
  45. [71]Giuste F. Shi W. Zhu Y. Naren T. Isgut M. Sha Y. Tong L. Gupte M. Wang M.D. Explainable artificial intelligence methods in combating pandemics: a systematic review IEEE Rev. Biomed. Eng. 2022 35737637
  46. [72]Reddy S. Allan S. Coghlan S. Cooper P. A governance model for the application of ai in health care J. Am. Med. Inform. Assoc. 27 2020 491 497 31682262 PMC7647243
  47. [75]Forcier M.B. Gallois H. Mullan S. Joly Y. Integrating artificial intelligence into health care through data access: can the gdpr act as a beacon for policymakers? J. Law Biosci. 6 2019 317 335 31666972 PMC6813940
  48. [81]Habli I. Lawton T. Porter Z. Artificial intelligence in health care: accountability and safety Bull. World Health Organ. 98 2020 251 32284648 PMC7133468
  49. [82]Martelli N. Eskenazy D. Déan C. Pineau J. Prognon P. Chatellier G. Sapoval M. Pellerin O. New European regulation for medical devices: what is changing? Cardiovasc. Interv. Radiol. 42 2019 1272 1278 31123774
  50. [83]Migliore A. On the new regulation of medical devices in Europe Expert Rev. Med. Devices 14 2017 921 923 29157016
  51. [84]Goold S.D. Lipkin M. Jr The doctor–patient relationship: challenges, opportunities, and strategies J. Gen. Intern. Med. 14 1999 S26 9933492 PMC1496871
  52. [90]Topol E.J. High-performance medicine: the convergence of human and artificial intelligence Nat. Med. 25 2019 44 56 30617339
  53. [93]Grote T. Berens P. On the ethics of algorithmic decision-making in healthcare J. Med. Ethics 46 2020 205 211 31748206 PMC7042960
  54. [95]Braun M. Hummel P. Beck S. Dabrock P. Primer on an ethics of ai-based decision support systems in the clinic J. Med. Ethics 47 2021 e3–3 PMC8639945 32245804
  55. [96]Babic B. Gerke S. Evgeniou T. Cohen I.G. Beware explanations from ai in health care Science 373 2021 284 286 34437144
  56. [100]Layman E.J. Ethical issues and the electronic health record Heal. Care Manag. 27 2008 165 176 18475119
  57. [101]De Lusignan S. Liaw S.-T. Krause P. Curcin V. Vicente M.T. Michalakidis G. Agreus L. Leysen P. Shaw N. Mendis K. Key concepts to assess the readiness of data for international research: data quality, lineage and provenance, extraction and processing errors, traceability, and curation Yearb. Med. Inform. 20 2011 112 120 21938335
  58. [102]Liaw S.-T. Powell-Davies G. Pearce C. Britt H. McGlynn L. Harris M.F. Optimising the use of observational electronic health record data: current issues, evolving opportunities, strategies and scope for collaboration Aust. Fam. Phys. 45 2016 153 156 27052055
  59. [104]Benchoufi M. Ravaud P. Blockchain technology for improving clinical research quality Trials 18 2017 1 5 28724395 PMC5517794
  60. [105]Prasser F. Spengler H. Bild R. Eicher J. Kuhn K.A. Privacy-enhancing etl-processes for biomedical data Int. J. Med. Inform. 126 2019 72 81 31029266
  61. [106]Culnane C. Rubinstein B.I. Teague V. Health data in an open world arXiv preprint arXiv:1712.05627 2017 · arXiv
  62. [107]Guo G.N. Jonnagaddala J. Farshid S. Huser V. Reich C. Liaw S.-T. Comparison of the cohort selection performance of Australian medicines terminology to anatomical therapeutic chemical mappings J. Am. Med. Inform. Assoc. 26 2019 1237 1246 31545380 PMC7647230
  63. [108]Liaw S.-T. Liyanage H. Kuziemsky C. Terry A.L. Schreiber R. Jonnagaddala J. de Lusignan S. Ethical use of electronic health record data and artificial intelligence: recommendations of the primary care informatics working group of the international medical informatics association Yearb. Med. Inform. 29 2020 051 PMC7442527 32303098
  64. [109]Lin S.Y. Mahoney M.R. Sinsky C.A. Ten ways artificial intelligence will transform primary care J. Gen. Intern. Med. 34 2019 1626 1630 31090027 PMC6667610

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Authors
Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M.
Venue
Heliyon
Publisher
Elsevier BV
Published
2024-02
Volume
10
Issue
4
Pages
e26297
ISSN
2405-8440
DOI
10.1016/j.heliyon.2024.e26297
PMC
PMC10879008
Cited by
1,083
References
9
Language
en
Licence
Funders
Università Campus Bio-Medico di Roma

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