Artificial intelligence in cancer care: a qualitative assessment of researchers’ awareness around AI-driven bias and its impact on health equity

G. El Khoury; L. Zacharias; S. Nabhani-Gebara

doi:10.1016/j.jemep.2026.101247

Artificial intelligence in cancer care: a qualitative assessment of researchers’ awareness around AI-driven bias and its impact on health equity

G. El Khoury
, L. Zacharias
, S. Nabhani-Gebara

Kingston University

Research output: Contribution to journal › Article › peer-review

Abstract

Background

Modern medicine has broadened its approach to cancer care and now exploits the use of Artificial Intelligence (AI) and its applications in health imaging and oncology. The AI for Health Imaging (AI4HI), a network comprised of six EU-funded research projects, targets the construction of robust AI infrastructures to advance cancer care. AI4HI involves clinicians, legal and technical researchers. The application of AI within practical medicine gives rise to ethical concerns as it can exacerbate systematic biases and health inequities. This study focused on assessing researchers’ awareness around AI-driven biases and their impact on health equity.

Methods

A qualitative research study was conducted among 24 stakeholders from 6 countries to explore their perceptions on AI-driven biases and measures to overcome them. Participants were recruited from the AI4HI network group through purposive sampling. The developed interview guide was based on an existing framework for the ethical development and application of AI/ML. Descriptive statistics were performed using SPSS. The qualitative data was analysed using NVivo 12 software, and thematic analysis was performed. Ethical approval was obtained for this study.

Results

Most of the participants were female (58.3%) and from Greece (41.7%). A lack of shared understanding was noted among the professionals on the definition of bias in AI. Limited diverse patient representation was identified as a main source of bias. A multidisciplinary approach and the involvement of legal experts to navigate through data protection laws and ensure broader data inclusion, was also highlighted.

Conclusion

The findings call for an urgent need to align researchers’ understanding of the concept of AI-driven Bias. Additionally, there is a need to proactively design and implement mitigation measures to limit various forms of bias and promote equitable AI in healthcare.

Original language	English
Article number	101247
Journal	Ethics, Medicine and Public Health
Volume	34
Early online date	11 Mar 2026
DOIs	https://doi.org/10.1016/j.jemep.2026.101247
Publication status	Published - 2026

Bibliographical note

This work was supported by the European Commission under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 952179.

Keywords

AI-driven biases
AI4HI network
Artificial intelligence
Health equity

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10.1016/j.jemep.2026.101247Licence: CC BY

Cite this

@article{09bb10cf574d4fafbbdbf284173293f5,

title = "Artificial intelligence in cancer care: a qualitative assessment of researchers{\textquoteright} awareness around AI-driven bias and its impact on health equity",

abstract = "Background Modern medicine has broadened its approach to cancer care and now exploits the use of Artificial Intelligence (AI) and its applications in health imaging and oncology. The AI for Health Imaging (AI4HI), a network comprised of six EU-funded research projects, targets the construction of robust AI infrastructures to advance cancer care. AI4HI involves clinicians, legal and technical researchers. The application of AI within practical medicine gives rise to ethical concerns as it can exacerbate systematic biases and health inequities. This study focused on assessing researchers{\textquoteright} awareness around AI-driven biases and their impact on health equity. Methods A qualitative research study was conducted among 24 stakeholders from 6 countries to explore their perceptions on AI-driven biases and measures to overcome them. Participants were recruited from the AI4HI network group through purposive sampling. The developed interview guide was based on an existing framework for the ethical development and application of AI/ML. Descriptive statistics were performed using SPSS. The qualitative data was analysed using NVivo 12 software, and thematic analysis was performed. Ethical approval was obtained for this study. Results Most of the participants were female (58.3\%) and from Greece (41.7\%). A lack of shared understanding was noted among the professionals on the definition of bias in AI. Limited diverse patient representation was identified as a main source of bias. A multidisciplinary approach and the involvement of legal experts to navigate through data protection laws and ensure broader data inclusion, was also highlighted. Conclusion The findings call for an urgent need to align researchers{\textquoteright} understanding of the concept of AI-driven Bias. Additionally, there is a need to proactively design and implement mitigation measures to limit various forms of bias and promote equitable AI in healthcare.",

keywords = "AI-driven biases, AI4HI network, Artificial intelligence, Health equity",

author = "\{El Khoury\}, G. and L. Zacharias and S. Nabhani-Gebara",

note = "This work was supported by the European Commission under the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement No. 952179.",

year = "2026",

doi = "10.1016/j.jemep.2026.101247",

language = "English",

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T2 - a qualitative assessment of researchers’ awareness around AI-driven bias and its impact on health equity

AU - El Khoury, G.

AU - Zacharias, L.

AU - Nabhani-Gebara, S.

N1 - This work was supported by the European Commission under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 952179.

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N2 - Background Modern medicine has broadened its approach to cancer care and now exploits the use of Artificial Intelligence (AI) and its applications in health imaging and oncology. The AI for Health Imaging (AI4HI), a network comprised of six EU-funded research projects, targets the construction of robust AI infrastructures to advance cancer care. AI4HI involves clinicians, legal and technical researchers. The application of AI within practical medicine gives rise to ethical concerns as it can exacerbate systematic biases and health inequities. This study focused on assessing researchers’ awareness around AI-driven biases and their impact on health equity. Methods A qualitative research study was conducted among 24 stakeholders from 6 countries to explore their perceptions on AI-driven biases and measures to overcome them. Participants were recruited from the AI4HI network group through purposive sampling. The developed interview guide was based on an existing framework for the ethical development and application of AI/ML. Descriptive statistics were performed using SPSS. The qualitative data was analysed using NVivo 12 software, and thematic analysis was performed. Ethical approval was obtained for this study. Results Most of the participants were female (58.3%) and from Greece (41.7%). A lack of shared understanding was noted among the professionals on the definition of bias in AI. Limited diverse patient representation was identified as a main source of bias. A multidisciplinary approach and the involvement of legal experts to navigate through data protection laws and ensure broader data inclusion, was also highlighted. Conclusion The findings call for an urgent need to align researchers’ understanding of the concept of AI-driven Bias. Additionally, there is a need to proactively design and implement mitigation measures to limit various forms of bias and promote equitable AI in healthcare.

AB - Background Modern medicine has broadened its approach to cancer care and now exploits the use of Artificial Intelligence (AI) and its applications in health imaging and oncology. The AI for Health Imaging (AI4HI), a network comprised of six EU-funded research projects, targets the construction of robust AI infrastructures to advance cancer care. AI4HI involves clinicians, legal and technical researchers. The application of AI within practical medicine gives rise to ethical concerns as it can exacerbate systematic biases and health inequities. This study focused on assessing researchers’ awareness around AI-driven biases and their impact on health equity. Methods A qualitative research study was conducted among 24 stakeholders from 6 countries to explore their perceptions on AI-driven biases and measures to overcome them. Participants were recruited from the AI4HI network group through purposive sampling. The developed interview guide was based on an existing framework for the ethical development and application of AI/ML. Descriptive statistics were performed using SPSS. The qualitative data was analysed using NVivo 12 software, and thematic analysis was performed. Ethical approval was obtained for this study. Results Most of the participants were female (58.3%) and from Greece (41.7%). A lack of shared understanding was noted among the professionals on the definition of bias in AI. Limited diverse patient representation was identified as a main source of bias. A multidisciplinary approach and the involvement of legal experts to navigate through data protection laws and ensure broader data inclusion, was also highlighted. Conclusion The findings call for an urgent need to align researchers’ understanding of the concept of AI-driven Bias. Additionally, there is a need to proactively design and implement mitigation measures to limit various forms of bias and promote equitable AI in healthcare.

KW - AI-driven biases

KW - AI4HI network

KW - Artificial intelligence

KW - Health equity

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DO - 10.1016/j.jemep.2026.101247

M3 - Article

AN - SCOPUS:105032475642

SN - 2352-5533

VL - 34

JO - Ethics, Medicine and Public Health

JF - Ethics, Medicine and Public Health

M1 - 101247

ER -

Artificial intelligence in cancer care: a qualitative assessment of researchers’ awareness around AI-driven bias and its impact on health equity

Abstract

Bibliographical note

Keywords

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