# Navigating Ethical Frontiers of AI-Driven Clinical Decision Support Systems: Exploring Explainability and Bias

> **NIH NIH R21** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2024 · $144,672

## Abstract

PROJECT SUMMARY
Note: This project is specific to bioethics research.
Existing National Comprehensive Cancer Network (NCCN) guidelines to screen patients for
genetic testing that are based on family history and personal risk factors remain highly inaccurate
with a positive predictive value (PPV) of less than 10%. Emerging AI models based on imaging
data already show great potential to improve the effectiveness of screening patients (with
improved PPV and reduced missed detections). However, the bioethics of AI-driven models and
their implementation in patient care remain unregulated and unexplored. This research will
research the bioethical concerns arising from using black-box AI models for stratifying
newly diagnosed breast cancer patients for genetic mutation and communicating test
results.
NCCN has established guidelines on who is eligible for genetic testing. However, due to the
inconsistent testing guidelines, more than 90% of the one million women in the U.S. who are
estimated to have a BRCA mutation remain undiagnosed. Universal testing has been proposed
as a possible solution. However, it is not economically feasible as it may incur up to $400 billion
to perform genetic testing for all U.S. women, not to mention the lack of genetic testing for low-
income populations--a large portion of population-at-risk. There are already ethical challenges
that surround genetic testing, and without timely intervention, AI might reinforce or even
exacerbate the outcome of patients who are already vulnerable due to existing disparities in the
current healthcare system. To this end, we will focus on the following specific aims: SA1: Analyze
Ethical Guidelines and Concerns with AI in Genetic Screening to assess the ethical concerns
of integrating AI in recommending genetic testing and communicating the results with the patients.
SA2: Managing AI Ethics–Generating Explanations, Minimizing Bias by investigating how
different methods of generating explanations and best practices to minimize bias towards certain
racial/ethnic groups could mitigate the ethical concerns of AI.
Understanding the ethical consequences of AI in genetic screening and steps to mitigating them
will be crucial in enhancing the transparency and trust in emerging healthcare technologies and
guiding future policies toward effectively integrating AI in medical decision-making.

## Key facts

- **NIH application ID:** 11064608
- **Project number:** 3R21EB033923-01S1
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** Ashif Iquebal
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $144,672
- **Award type:** 3
- **Project period:** 2023-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11064608

## Citation

> US National Institutes of Health, RePORTER application 11064608, Navigating Ethical Frontiers of AI-Driven Clinical Decision Support Systems: Exploring Explainability and Bias (3R21EB033923-01S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11064608. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*