Skip to main content
SpringerLink
Log in

Dermatological diagnostic-assistive technologies: a call for regulatory action

  • Original Research
  • Published:
AI and Ethics Aims and scope Submit manuscript

Abstract

Artificial intelligence (AI) has been increasingly implemented within the healthcare sector. In that regard, efforts are being made toward commercialising diagnostic-assistive technologies. The ability of such systems to mine a significant amount of clinical data has enabled diagnostics to become progressively more precise. Nevertheless, the usage of this technology poses critical challenges regarding ethical, legal and sociological aspects that ought to be addressed and call for regulation. Diagnostic-assistive technologies may be implemented in multiple healthcare fields, all of which require the policymakers’ attention. Despite having transversal concerns, studying the impact of this technology for dermatological diagnosis purposes is paramount, considering that it poses a specific concern in this particular field of application: discrimination. Algorithmic biases such as misrepresentation due to insufficient data for data training processes render the accuracy of the diagnosis provided by this technology selective which favours one group of people over another, solely based on skin colour (a physiognomic feature highly relevant to this field of medicine). Moreover, this technology poses relevant questions regarding the inefficiency of the current framework on liability, such as the Product Liability Directive, which establishes inadequate regimes to encompass these instruments. Additionally, by being so onerous to the healthcare structure, the current legislation enables physicians to adopt defensive behaviours, which tend to favour their patients less. This article aims at understanding how regulatory actions can prevent the lack of legal certainty and social concerns regarding using dermatological diagnostic-assistive technologies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

The dataset used for this study regarding the demonstration of the accuracy of a specific dermatological diagnostic-assistive technology powered by a convolutional neural network (CNN) in detecting skin cancer may be found in S Albawi et al., Understanding of a convolutional neural network. International Conference of Engineering and Technology (ICET) 1–6 (2017).

Notes

  1. IBM Website, ‘How AI is impacting healthcare’. Website How AI is impacting healthcare | Watson Health | IBM, accessed on 3rd May 2022 [15].

  2. GS Guthart and S. JK. The Intuitive telesurgery system: Overview and application. Proc 2000 ICRA Millenn Conf IEEE Int Conf Robot Autom Symp Proc. 1: 618–621 [3].

  3. Policy Department for Citizens' Rights and Constitutional Affairs Directorate-General for Internal Policies, ‘Artificial Intelligence and Civil Liability’, 2020 [21].

  4. Ibid.

  5. Scientific American, 'Health Care AI Systems Are Biased'. Website Health Care AI Systems Are Biased—Scientific American, accessed on 3rd May 2022 [17].

  6. A Esteva, B Kuprel, RA Novoa, 'Dermatologist-level classification of skin cancer with deep neural networks. (2017) 542 Nature 115–118 [1].

  7. S Gerke et al., 'Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare 295–336 (2020) [10].

  8. Scientific American, 'Health Care AI Systems Are Biased'. Website Health Care AI Systems Are Biased—Scientific American, accessed on 23rd May 2022 [17].

  9. Ibid.

  10. World Health Organisation, Health product and policy standards, 'Access to assistive technology & medical devices'. Website https://www.who.int/teams/health-product-policy-and-standards/assistive-and-medical-technology, accessed on 23rd May 2022 [18].

  11. R Williams, 'What is Assistive Tech, who is it for, and what does it enable?' [2018]. Website https://at2030.org/what-is-assistive-tech,-who-is-it-for,-and-what-does-it-enable?/, accessed on 23rd May 2022 [16].

  12. World Health Organisation, 'Assistive Technology: What is in a name?' [2020]. Website https://at2030.org/assistive-technology,-what-is-in-a-name?/, accessed on 23rd May 2022 [19].

  13. A Esteva, B Kuprel, RA Novoa, 'Dermatologist-level classification of skin cancer with deep neural networks'. 542 Nature 115–118 (2017) [1].

  14. S Jartarkar et al., 'New diagnostic and imaging technologies in dermatology. Journal of Cosmetic Dermatology 20(12):3782–3787 (2021) [11].

  15. A Esteva, B Kuprel, RA Novoa, 'Dermatologist-level classification of skin cancer with deep neural networks'.  542 Nature 115–118 (2017) [1].

  16. S Albawi et al., Understanding of a convolutional neural network. International Conference of Engineering and Technology (ICET) 1–6 (2017) [8].

  17. R Chakravorty et al., 'Exploiting Local and Generic Features for Accurate Skin Lesions Classification Using Clinical and Dermoscopy Imaging. International Symposium on Biomedical Imaging (2017) [5].

  18. A Bowling, 'Training Watson to Help Detect Melanomas Earlier and Faster' IBM Watson. Website https://www.ibm.com/blogs/think/2017/03/training-watson-to-detect-melanomas-earlier-and-faster/, accessed on 23rd May 2022 [13].

  19. Policy Department for Citizens' Rights and Constitutional Affairs Directorate-General for Internal Policies, ‘Artificial Intelligence and Civil Liability’, 2020 [21].

  20. Council Directive 85/374/EEC of 25 July 1985 on the approximation of the laws, regulations and administrative provisions of the Member States concerning liability for defective products [22].

  21. Case: VI v KRONE-Verlag Gesellschaft mbH and Co KG (Case C-65/20) ECLI:EU:C:2021:471 [24].

  22. European Commission considers new civil liability rules for the digital age and artificial intelligence. Website https://www.nortonrosefulbright.com/en-de/knowledge/publications/dfbc5dc8/european-commission-considers-new-civil-liability-rules, accessed on 23rd May 2022 [14].

  23. Policy Department for Citizens' Rights and Constitutional Affairs Directorate-General for Internal Policies, ‘Artificial Intelligence and Civil Liability’, 2020 [21].

  24. Ibid.

  25. B Koch, Medical Liability in Europe: Comparative Analysis Medical Liability in Europe A Comparison of Selected Jurisdictions ( edn, De Gruyter 2011) [2].

  26. S Dalton-brown, 'The Ethics of Medical AI and the Physician–Patient Relationship. Cambridge Quarterly of Healthcare Ethics 29(1):115–12 (2020) [9].

  27. European Parliament resolution of 16 February 2017 with recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL)), paragraph 32 [23].

  28. Policy Department for Citizens' Rights and Constitutional Affairs Directorate-General for Internal Policies, ‘Artificial Intelligence and Civil Liability’, 2020 [21].

  29. A Esteva, B Kuprel, RA Novoa, 'Dermatologist-level classification of skin cancer with deep neural networks'. 542 Nature 115–118 (2017) [1].

  30. Scientific American, 'Health Care AI Systems Are Biased'. Website Health Care AI Systems Are Biased—Scientific American, accessed on 24th May 2022 [17].

  31. R Daneshjou et al., Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set [6].

  32. Z Villines, 'Skin cancer by race and ethnicity' [2021] MedicalNewsToday. Website https://www.medicalnewstoday.com/articles/skin-cancer-by-race, accessed on 24th May 2022 [20].

  33. T. Beauchamp and J. Childress, Principles of Biomedical Ethics (7th edn, Oxford University Press 2016) [17].

  34. Scientific American, 'Health Care AI Systems Are Biased'. Website Health Care AI Systems Are Biased—Scientific American, accessed on 24th May 2022 [17].

  35. L Lessig, ‘The Law of the Horse: What Cyberlaw Might Teach’ (1999) 6 Harvard Law Review 501 [4].

  36. Ronald Leenes, Erica Palmerini, Bert-Jaap Koops, Andrea Bertolini, Pericle Salvini & Federica Lucivero (2017) Regulatory challenges of robotics: some guidelines for addressing legal and ethical issues, Law, Innovation and Technology, 9:1, 1–44, https://doi.org/10.1080/17579961.2017.1304921 [7].

References

  1. Esteva, A., Kuprel, B., Novoa, R.A.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)

    Article  Google Scholar 

  2. Koch, B.: Medical liability in Europe: comparative analysis medical liability in Europe A comparison of selected jurisdictions. De Gruyter (2011)

    Book  Google Scholar 

  3. Guthart, G.S., JK, S.: The Intuitive telesurgery system: overview and application. In: Proc 2000 ICRA Millenn Conf IEEE Int Conf Robot Autom Symp. 1:618–621 (2000)

  4. Lessig, L.: The law of the horse: what cyberlaw might teach. Harvard Law Rev. 6, 501 (1999)

    Article  Google Scholar 

  5. Chakravorty, R. et al.: Exploiting local and generic features for accurate skin lesions classification using clinical and dermoscopy imaging. In: International Symposium on Biomedical Imaging (2017)

  6. Daneshjou, R. et al.: Disparities in dermatology AI performance on a diverse, curated clinical image set

  7. Leenes, R., Palmerini, E., Koops, B.-J., Bertolini, A., Salvini, P., Lucivero, F.: Regulatory challenges of robotics: some guidelines for addressing legal and ethical issues. Law Innov. Technol. 9, 1 (2017)

    Article  Google Scholar 

  8. Albawi, S. et al.: Understanding of a convolutional neural network. In: International Conference of Engineering and Technology (ICET) (2017)

  9. Dalton-Brown, S.: The ethics of medical AI and the physician-patient relationship. Camb Q Healthc Ethics 29(1), 115–121 (2020)

    Article  Google Scholar 

  10. Gerke, S., et al.: Ethical and legal challenges of artificial intelligence-driven healthcare. In: Artificial intelligence in healthcare, pp. 295–336. Academic Press (2020)

    Chapter  Google Scholar 

  11. Jartarkar, S., et al.: New diagnostic and imaging technologies in dermatology. J. Cosmet. Dermatol. 20(12), 3782–3787 (2021)

    Article  Google Scholar 

  12. Beauchamp, T., Childress, J.: Principles of biomedical ethics, 7th edn. Oxford University Press (2016)

    Google Scholar 

  13. Bowling, A.: Training Watson to help detect melanomas earlier and faster. IBM Watson. Website https://www.ibm.com/blogs/think/2017/03/training-watson-to-detect-melanomas-earlier-and-faster/

  14. European Commission considers new civil liability rules for the digital age and artificial intelligence Website https://www.nortonrosefulbright.com/en-de/knowledge/publications/dfbc5dc8/european-commission-considers-new-civil-liability-rules

  15. IBM Website: How AI is impacting healthcare. How AI is impacting healthcare|Watson Health|IBM

  16. Williams, R.: What is assistive tech, who is it for, and what does it enable? (2018) Website https://at2030.org/what-is-assistive-tech,-who-is-it-for,-and-what-does-it-enable?

  17. Scientific American: Health care AI systems are biased. Website Health Care AI Systems Are Biased—Scientific American

  18. World Health Organisation: Health product and policy standards. Access to assistive technology & medical devices. Website https://www.who.int/teams/health-product-policy-and-standards/assistive-and-medical-technology.

  19. World Health Organisation: Assistive technology: what is in a name? (2020). Website https://at2030.org/assistive-technology,-what-is-in-a-name?

  20. Villines, Z.: Skin cancer by race and ethnicity. MedicalNewsToday (2021). Website https://www.medicalnewstoday.com/articles/skin-cancer-by-race

  21. Policy Department for Citizens' Rights and Constitutional Affairs Directorate-General for Internal Policies. Artificial Intelligence and Civil Liability (2020)

  22. Council Directive 85/374/EEC of 25 July 1985 on the approximation of the laws, regulations and administrative provisions of the Member States concerning liability for defective products

  23. European Parliament resolution of 16 February 2017 with recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL))

  24. Case: VI v KRONE-Verlag Gesellschaft mbH and Co KG (Case C-65/20) ECLI:EU:C:2021:471

Download references

Author information

Authors and Affiliations

  1. Tilburg University, Tilburg, The Netherlands

    Maria do Rosário Pinto-Alves

  2. Catholic University of Portugal, Lisbon, Portugal

    Maria do Rosário Pinto-Alves

Authors
  1. Maria do Rosário Pinto-Alves
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria do Rosário Pinto-Alves.

Additional information

Publisher's Note

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

Adapted essay written as an evaluation element for the course of Health, Care and Regulation, at Tilburg University (2022).

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

Pinto-Alves, M. Dermatological diagnostic-assistive technologies: a call for regulatory action. AI Ethics (2023). https://doi.org/10.1007/s43681-023-00262-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s43681-023-00262-z

Keywords

Search

Navigation