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Addressing Intersectionality, Explainability, and Ethics in AI-Driven Diagnostics: A Rebuttal and Call for Transdiciplinary Action

Myles Joshua Toledo Tan, Panayiotis V. Benos

TL;DR

This paper critiques the focus on diagnostic accuracy in AI-driven medical diagnostics and argues for integrating intersectionality, privacy, and transdisciplinary perspectives to address fairness and ethics. It advocates a framework that includes intersectional fairness, determinants of health, privacy and security, and transdisciplinary collaboration, with a strong emphasis on explainability and participatory design. The authors propose concrete recommendations (6.1–6.5) to ensure equity-centered AI diagnostics that reflect diverse populations and real-world contexts. The work highlights the practical impact of incorporating broader social determinants of health and robust governance to build trust and prevent exacerbating health disparities.

Abstract

The increasing integration of artificial intelligence (AI) into medical diagnostics necessitates a critical examination of its ethical and practical implications. While the prioritization of diagnostic accuracy, as advocated by Sabuncu et al. (2025), is essential, this approach risks oversimplifying complex socio-ethical issues, including fairness, privacy, and intersectionality. This rebuttal emphasizes the dangers of reducing multifaceted health disparities to quantifiable metrics and advocates for a more transdisciplinary approach. By incorporating insights from social sciences, ethics, and public health, AI systems can address the compounded effects of intersecting identities and safeguard sensitive data. Additionally, explainability and interpretability must be central to AI design, fostering trust and accountability. This paper calls for a framework that balances accuracy with fairness, privacy, and inclusivity to ensure AI-driven diagnostics serve diverse populations equitably and ethically.

Addressing Intersectionality, Explainability, and Ethics in AI-Driven Diagnostics: A Rebuttal and Call for Transdiciplinary Action

TL;DR

This paper critiques the focus on diagnostic accuracy in AI-driven medical diagnostics and argues for integrating intersectionality, privacy, and transdisciplinary perspectives to address fairness and ethics. It advocates a framework that includes intersectional fairness, determinants of health, privacy and security, and transdisciplinary collaboration, with a strong emphasis on explainability and participatory design. The authors propose concrete recommendations (6.1–6.5) to ensure equity-centered AI diagnostics that reflect diverse populations and real-world contexts. The work highlights the practical impact of incorporating broader social determinants of health and robust governance to build trust and prevent exacerbating health disparities.

Abstract

The increasing integration of artificial intelligence (AI) into medical diagnostics necessitates a critical examination of its ethical and practical implications. While the prioritization of diagnostic accuracy, as advocated by Sabuncu et al. (2025), is essential, this approach risks oversimplifying complex socio-ethical issues, including fairness, privacy, and intersectionality. This rebuttal emphasizes the dangers of reducing multifaceted health disparities to quantifiable metrics and advocates for a more transdisciplinary approach. By incorporating insights from social sciences, ethics, and public health, AI systems can address the compounded effects of intersecting identities and safeguard sensitive data. Additionally, explainability and interpretability must be central to AI design, fostering trust and accountability. This paper calls for a framework that balances accuracy with fairness, privacy, and inclusivity to ensure AI-driven diagnostics serve diverse populations equitably and ethically.
Paper Structure (12 sections, 1 figure)

This paper contains 12 sections, 1 figure.

Table of Contents

  1. Introduction
  2. The Centrality of Intersectionality in Fairness and Diagnostics
  3. Privacy and Security: Beyond Technical Compliance
  4. Granularity and Practicality
  5. The Ethical Role of Engineers in AI Development
  6. Recommendations for an Inclusive and Ethical Framework
  7. Embrace Intersectional Fairness
  8. Adopt Privacy-First Principles
  9. Foster Transdisciplinary Collaboration
  10. Prioritize Explainability
  11. Promote Participatory AI Development
  12. Conclusion

Figures (1)

  • Figure 1: Framework for an Equity-Centered and AI-Driven Diagnostics. This framework illustrates the four pillars essential for achieving equitable and inclusive AI-driven diagnostics. These include Intersectional Fairness, Determinants of Health Integration (social; environmental; behavioral and lifestyle; economic and structural; and cultural and contextual), Privacy and Security, and Transdisciplinary Collaboration. The core goal is to create AI diagnostic systems that prioritize equity while addressing real-world complexities in healthcare.