Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Artificial intelligence and urology: ethical considerations for urologists and patients

Nature Reviews Urology volume 21, pages 50–59 (2024)Cite this article

Subjects

Abstract

The use of artificial intelligence (AI) in medicine and in urology specifically has increased over the past few years, during which time it has enabled optimization of patient workflow, increased diagnostic accuracy and enhanced computer analysis of radiological and pathological images. However, before further use of AI is undertaken, possible ethical issues need to be evaluated to improve understanding of this technology and to protect patients and providers. Possible ethical issues that require consideration when applying AI in clinical practice include patient safety, cybersecurity, transparency and interpretability of the data, inclusivity and equity, fostering responsibility and accountability, and the preservation of providers’ decision-making and autonomy. Ethical principles for the application of AI to health care and in urology are proposed to guide urologists, patients and regulators to improve use of AI technologies and guide policy-making.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Recommendations for improving the ethics of use of AI in urology.

References

  1. Nolsøe, A. B., Østergren, P. B., Jensen, C. F. S. & Fode, M. From separation to collaboration: the future of urology. Nat. Rev. Urol. 16, 633–634 (2019).

    Article  PubMed  Google Scholar 

  2. Sidey-Gibbons, J. A. M. & Sidey-Gibbons, C. J. Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19, 64 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  3. World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance (WHO, 2021).

  4. Gillon, R. Medical ethics: four principles plus attention to scope. Br. Med. J. 309, 184 (1994).

    Article  CAS  Google Scholar 

  5. Benanti, P. Algor-ethics: artificial intelligence and ethical reflection. Rev. Éthique Théol. Morale 307, 93–110 (2020).

    Article  Google Scholar 

  6. Cacciamani, G. E., Anvar, A., Chen, A., Gill, I. & Hung, A. J. How the use of the artificial intelligence could improve surgical skills in urology: state of the art and future perspectives. Curr. Opin. Urol. 31, 378–384 (2021).

    Article  PubMed  Google Scholar 

  7. Chen, A. B. et al. Artificial intelligence applications in urology: reporting standards to achieve fluency for urologists. Urol. Clin. North Am. 49, 65–117 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hung, A. J., Chen, A. B., Cacciamani, G. E. & Gill, I. S. Artificial intelligence will (may) make doctors expendable (in good ways): pro. Eur. Urol. Focus 7, 683–684 (2021).

    Article  PubMed  Google Scholar 

  9. Hung, A. J., Liu, Y. & Anandkumar, A. Deep learning to automate technical skills assessment in robotic surgery. JAMA Surg. 156, 1059–1060 (2021).

    Article  PubMed  Google Scholar 

  10. Rapakoulia, T. et al. EnsembleGASVR: a novel ensemble method for classifying missense single nucleotide polymorphisms. Bioinformatics 30, 2324–2333 (2014).

    Article  CAS  PubMed  Google Scholar 

  11. Johnson, H. et al. Development and validation of a 25-gene panel urine test for prostate cancer diagnosis and potential treatment follow-up. BMC Med. 18, 1–14 (2020).

    Article  Google Scholar 

  12. Hamet, P. & Tremblay, J. Artificial intelligence in medicine. Metabolism 69, S36–S40 (2017).

    Article  CAS  Google Scholar 

  13. Chen, A. B. et al. Artificial intelligence applications in urology: reporting standards to achieve fluency for urologists. Urol. Clin. North Am. 49, 65–117 (2022).

    Article  PubMed  Google Scholar 

  14. World Health Organization. WHO guidelines for safe surgery 2009: safe surgery saves lives (WHO, 2009).

  15. Gawande, A. A., Thomas, E. J., Zinner, M. J. & Brennan, T. A. The incidence and nature of surgical adverse events in Colorado and Utah in 1992. Surgery 126, 66–75 (1999).

    Article  CAS  PubMed  Google Scholar 

  16. Luongo, F., Hakim, R., Nguyen, J. H., Anandkumar, A. & Hung, A. J. Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery. Surgery 169, 1240–1244 (2021).

    Article  PubMed  Google Scholar 

  17. Szeliski, R. Computer Vision: Algorithms and Applications (Springer, 2010).

  18. Chen, J. et al. Objective assessment of robotic surgical technical skill: a systematic review. J. Urol. 201, 461–469 (2019).

    Article  PubMed  Google Scholar 

  19. Eppler, M. B. et al. Automated capture of intraoperative adverse events using artificial intelligence: a systematic review and meta-analysis. J. Clin. Med. 12, 1687 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Cacciamani, G. E. et al. Is artificial intelligence replacing our radiology stars? Not yet! Eur. Urol. Open Sci. 48, 14–16 (2023).

    Article  PubMed  Google Scholar 

  21. Bartsch, G. Jr. et al. Use of artificial intelligence and machine learning algorithms with gene expression profiling to predict recurrent nonmuscle invasive urothelial carcinoma of the bladder. J. Urol. 195, 493–498 (2016).

    Article  PubMed  Google Scholar 

  22. Wong, N. C., Lam, C., Patterson, L. & Shayegan, B. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int. 123, 51–57 (2019).

    Article  PubMed  Google Scholar 

  23. Evans, B. J., Burke, W. & Jarvik, G. P. The FDA and genomic tests — getting regulation right. N. Engl. J. Med. 372, 2258–2264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Toft, E. L., Kaae, S. E., Malmqvist, J. & Brodersen, J. Psychosocial consequences of receiving false-positive colorectal cancer screening results: a qualitative study. Scand. J. Prim. Health Care 37, 145–154 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kaissis, G. A., Makowski, M. R., Rückert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2, 305–311 (2020).

    Article  Google Scholar 

  26. Rudzicz, F. & Saqur, R. Ethics of artificial intelligence in surgery. Preprint at https://doi.org/10.48550/arXiv.2007.14302 (2020).

  27. Centers for Disease Control and Prevention. Health Insurance Portability and Accountability Act of 1996 (HIPAA). CDC https://www.cdc.gov/phlp/publications/topic/hipaa.html (1996).

  28. European Council. The General Data Protection Regulation. EC https://www.consilium.europa.eu/en/policies/data-protection/data-protection-regulation/ (2022).

  29. Cohen, I. G. & Mello, M. M. HIPAA and protecting health information in the 21st century. J. Am. Med. Assoc. 320, 231–232 (2018).

    Article  Google Scholar 

  30. Price, W. N. & Cohen, I. G. Privacy in the age of medical big data. Nat. Med. 25, 37–43 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. European Union. Artificial intelligence Act. EU https://artificialintelligenceact.eu/the-act/ (2021).

  32. Meszaros, J., Minari, J. & Huys, I. The future regulation of artificial intelligence systems in healthcare services and medical research in the European Union. Front. Genet. 13, 927721 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Hirsch, D. D. From individual control to social protection: new paradigms for privacy law in the age of predictive analytics. Md. Law Rev. 79, 439 (2019).

    Google Scholar 

  34. Rocher, L., Hendrickx, J. M. & De Montjoye, Y.-A. Estimating the success of re-identifications in incomplete datasets using generative models. Nat. Commun. 10, 1–9 (2019).

    Article  CAS  Google Scholar 

  35. Goldsteen, A., Ezov, G., Shmelkin, R., Moffie, M. & Farkash, A. Data minimization for GDPR compliance in machine learning models. AI Ethics 2, 477–479 (2022).

    Article  Google Scholar 

  36. Fredrikson, M. et al. in 23rd USENIX Security Symposium 17–32 (2014).

  37. Gerke, S., Yeung, S. & Cohen, I. G. Ethical and legal aspects of ambient intelligence in hospitals. J. Am. Med. Assoc. 323, 601–602 (2020).

    Article  Google Scholar 

  38. Tsang, L. et al. The impact of artificial intelligence on medical innovation in the European Union and United States. Intell. Prop. Technol. Law J. 29, 3–11 (2017).

    Google Scholar 

  39. US Food and Drug Administration. Firmware update to address cybersecurity vulnerabilities identified in Abbott’s (formerly St. Jude Medical’s) implantable cardiac pacemakers: FDA safety communication (FDA, 2017).

  40. Morgan, S. Cybercrime to cost the world $10.5 trillion annually by 2025. Cybersecurity Ventures https://cybersecurityventures.com/cybercrime-damages-6-trillion-by-2021 (2020).

  41. Williams, C. M., Chaturvedi, R. & Chakravarthy, K. Cybersecurity risks in a pandemic. J. Med. Internet Res. 22, e23692 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Kruse, C. S., Frederick, B., Jacobson, T. & Monticone, D. K. Cybersecurity in healthcare: a systematic review of modern threats and trends. Technol. Health Care 25, 1–10 (2017).

    Article  PubMed  Google Scholar 

  43. Simera, I. et al. Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network. BMC Med. 8, 24 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  44. UK EQUATOR Centre. Enhancing the quality and transparency of health research. EQUATOR Network https://www.equator-network.org (2023).

  45. Chen, J. et al. Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int. 124, 567–577 (2019).

    Article  PubMed  Google Scholar 

  46. Checcucci, E. et al. Applications of neural networks in urology: a systematic review. Curr. Opin. Urol. 30, 788–807 (2020).

    Article  PubMed  Google Scholar 

  47. Han, E. R. et al. Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BMC Med. Educ. 19, 460 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat. Med. 26, 1364–1374 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cruz Rivera, S. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat. Med. 26, 1351–1363 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Collins, G. S. et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11, e048008 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Vasey, B. et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat. Med. 28, 924–933 (2022).

    Article  CAS  PubMed  Google Scholar 

  52. UK EQUATOR Centre. Reporting guidelines under development for other study designs. EQUATOR Network https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-other-study-designs/#STARDAI (2020).

  53. Mongan, J., Moy, L. & Kahn, C. E. Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol. Artif. Intell. 2, e200029 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Cacciamani, G. E. et al. PRISMA AI reporting guidelines for systematic reviews and meta-analyses on AI in healthcare. Nat. Med. 29, 14–15 (2023).

    Article  CAS  PubMed  Google Scholar 

  55. Murphy, K. et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med. Ethics 22, 14 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Benanti, P. The urgency of an algorethics. Discov. Artif. Intell. 3, 11 (2023).

    Article  Google Scholar 

  57. Habli, I., Lawton, T. & Porter, Z. Artificial intelligence in health care: accountability and safety. Bull. World Health Organ. 98, 251–256 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Smith, H. Clinical AI: opacity, accountability, responsibility and liability. AI Soc. 36, 535–545 (2021).

    Article  Google Scholar 

  59. Science and Technology Committee. Robotics and artificial intelligence: fifth report of session 2016–2017 https://publications.parliament.uk/ (House of Commons, 2016).

  60. Liu, X. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1, e271–e297 (2019).

    Article  PubMed  Google Scholar 

  61. Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563–577 (2016).

    Article  PubMed  Google Scholar 

  62. van Timmeren, J. E., Cester, D., Tanadini-Lang, S., Alkadhi, H. & Baessler, B. Radiomics in medical imaging — “how-to” guide and critical reflection. Insights Imaging 11, 91 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Sugano, D. et al. Impact of radiomics on prostate cancer detection: a systematic review of clinical applications. Curr. Opin. Urol. 30, 754–781 (2020).

    Article  PubMed  Google Scholar 

  64. Adadi, A. & Berrada, M. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018).

    Article  Google Scholar 

  65. Morozov, A. et al. A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading. Prostate Cancer Prostatic Dis. https://doi.org/10.1038/s41391-023-00673-3 (2023).

    Article  PubMed  Google Scholar 

  66. US Food and Drug Administration. FDA authorizes software that can help identify prostate cancer (FDA, 2021).

  67. Raciti, P. et al. Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies. Mod. Pathol. 33, 2058–2066 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Glikson, E. & Woolley, A. W. Human trust in artificial intelligence: review of empirical research. Acad. Manag. Ann. 14, 627–660 (2020).

    Article  Google Scholar 

  69. Nundy, S., Montgomery, T. & Wachter, R. M. Promoting trust between patients and physicians in the era of artificial intelligence. J. Am. Med. Assoc. 322, 497–498 (2019).

    Article  Google Scholar 

  70. Doshi-Velez, F. & Kim, B. Towards a rigorous science of interpretable machine learning. Preprint at arXiv https://doi.org/10.48550/arXiv.1702.08608 (2017).

  71. Gastounioti, A. & Kontos, D. Is it time to get rid of black boxes and cultivate trust in AI? Radiol. Artif. Intell. 2, e200088 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Reyes, M. et al. On the interpretability of artificial intelligence in radiology: challenges and opportunities. Radiol. Artif. Intell. 2, e190043 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Hales, M. AI presents HIPAA risks. HIPAA E-Tool https://thehipaaetool.com/ai-presents-hipaa-risks/ (2023).

  74. European Parliament and Council of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (Official Journal of the European Union, 2016).

  75. Ghassemi, M., Oakden-Rayner, L. & Beam, A. L. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3, e745–e750 (2021).

    Article  CAS  PubMed  Google Scholar 

  76. Hamon, R. et al. in Proc. 2021 ACM Conf. Fairness Accountability Transparency 549–559 (ACM, 2021).

  77. Barocas, S. & Selbst, A. D. Big data’s disparate impact. Calif. L. Rev. 104, 671 (2016).

    Google Scholar 

  78. Malanga, S. E., Loe, J. D., Robertson, C. T. & Ramos, K. in Big Data, Health Law, and Bioethics (eds Cohen, G. et al.) 98–111 (Cambridge Univ. Press, 2018).

  79. Gijsberts, C. M. et al. Race/ethnic differences in the associations of the Framingham risk factors with carotid IMT and cardiovascular events. PLoS ONE 10, e0132321 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  80. McCarthy, A. M. et al. Health care segregation, physician recommendation, and racial disparities in BRCA1/2 testing among women with breast cancer. J. Clin. Oncol. 34, 2610 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Caliskan, A., Bryson, J. J. & Narayanan, A. Semantics derived automatically from language corpora contain human-like biases. Science 356, 183–186 (2017).

    Article  CAS  PubMed  Google Scholar 

  82. Banerjee, I. et al. Reading race: AI recognises patient’s racial identity in medical images. Preprint at arXiv https://doi.org/10.48550/arXiv.2107.10356 (2021).

  83. United Nations. Universal Declaration of Human Rights. United Nations Gen. Assem. 302, 14–25 (1948).

    Google Scholar 

  84. Office of the United Nations High Commissioner for Human Rights. The right to privacy in the digital age (OHCHR, 2022).

  85. Office of the United Nations High Commissioner for Human Rights. Artificial intelligence and privacy, and children’s privacy (OHCHR, 2021).

  86. Bacciarelli, A. et al. The Toronto Declaration: protecting the right to equality and non-discrimination in machine learning systems (Amnesty Int., 2018).

  87. Andorno, R. The Oviedo Convention: a European legal framework at the intersection of human rights and health law (De Gruyter, 2005).

  88. Altman, D. G., Simera, I., Hoey, J., Moher, D. & Schulz, K. EQUATOR: reporting guidelines for health research. Lancet 371, 1149–1150 (2008).

    Article  PubMed  Google Scholar 

  89. Liu, X., Faes, L., Calvert, M. J. & Denniston, A. K. Extension of the CONSORT and SPIRIT statements. Lancet 394, 1225 (2019).

    Article  PubMed  Google Scholar 

  90. Committee on Artificial Intelligence. Consolidated working draft of the Framework Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law (Council of Europe, 2023).

  91. Council of Europe. Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data (Council of Europe, 1981).

  92. European Commission for the Efficiency of Justice. European ethical Charter on the use of artificial intelligence in judicial systems and their environment (Council of Europe, 2018).

  93. Organisation for Economic Co-operation and Development. Recommendation of the Council on Artificial Intelligence (OECD, 2019).

  94. Ibero-American Data Protection Network. General recommendations for the processing of personal data in artificial intelligence (redipd, 2019).

Download references

Author information

Authors and Affiliations

  1. The Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Giovanni E. Cacciamani, Andrew Chen, Inderbir S. Gill & Andrew J. Hung

  2. AI Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA

    Giovanni E. Cacciamani, Andrew Chen, Inderbir S. Gill & Andrew J. Hung

  3. Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Giovanni E. Cacciamani

Authors
  1. Giovanni E. Cacciamani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Inderbir S. Gill
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew J. Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.E.C., A.C. and A.J.H. researched data for the article. All authors contributed substantially to discussion of the content. G.E.C., A.C. and A.J.H. wrote the article. G.E.C., I.S.G. and A.J.H. reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Giovanni E. Cacciamani.

Ethics declarations

Competing interests

I.S.G. is an unpaid advisor for Steba and has equity in OneLine Health. A.J.H. is a paid advisor for Intuitive Surgical. A.C. and G.E.C. declare no competing interests.

Peer review

Peer review information

Nature Reviews Urology thanks Alfredo Vellido, Alejandro Granados Martinez and Juan Gomez Rivas for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cacciamani, G.E., Chen, A., Gill, I.S. et al. Artificial intelligence and urology: ethical considerations for urologists and patients. Nat Rev Urol 21, 50–59 (2024). https://doi.org/10.1038/s41585-023-00796-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41585-023-00796-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research