II
Imane Ihaddouchen
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
Responsible artificial intelligence in healthcare
A systematic review on the use of ethical principles in the development and deployment of artificial intelligence
Journal article
(2025)
-
Imane Ihaddouchen, S.N.R. Buijsman, G. Pozzi, D. van de Sande, A.A. Reis, R. Townsend, M.J. van den Hoven, D. Gommers, M. E. van Genderen
Objective
As hospitals increasingly adopt artificial intelligence (AI) to manage rising patient volumes, workforce shortages and healthcare costs, concerns about ethical implementation have become prominent. This systematic review aims to assess how hospital-focused AI literature addresses the WHO’s six ethical AI principles—autonomy; well-being and safety; transparency and explainability; responsibility and accountability; inclusiveness and equity; and responsiveness and sustainability.
Methods and analysis
A systematic review (PROSPERO registration: CRD42022347871) was conducted by searching Embase, MEDLINE ALL, Web of Science and the Cochrane Central Register of Controlled Trials from inception to December 2023, supplemented by Google Scholar. English-language studies describing AI (machine learning, deep learning, predictive analytics) relevant to inpatient settings and referencing at least one WHO principle were included. Two reviewers independently screened titles, abstracts and full texts, extracting data on publication year, country, study design, AI type, technology readiness level and ethical considerations. Discrepancies were resolved by consensus.
Results
Of 4770 unique records, 673 were included. Most (83%) originated from high-income countries, with publication volume rising sharply after 2021. Of these, 558 (83%) addressed at least one WHO principle in depth, most frequently inclusiveness and equity (49%), transparency and explainability (45%) and autonomy (42%). Well-being and safety (26%) and responsibility and accountability (29%) were less frequently covered, while responsiveness and sustainability (6%) was rarely explored. Among 44 studies developing AI applications with technology readiness levels 1–6, ethical principles were acknowledged but rarely operationalised.
Conclusion
Hospital-based AI research demonstrates increasing attention to ethical principles but lacks comprehensive application, particularly regarding sustainability. High-income countries dominate this discourse, underscoring the need for broader global engagement. To achieve equitable, safe and sustainable AI in clinical practice, clearer operational guidance and more inclusive collaboration is warranted. ...
As hospitals increasingly adopt artificial intelligence (AI) to manage rising patient volumes, workforce shortages and healthcare costs, concerns about ethical implementation have become prominent. This systematic review aims to assess how hospital-focused AI literature addresses the WHO’s six ethical AI principles—autonomy; well-being and safety; transparency and explainability; responsibility and accountability; inclusiveness and equity; and responsiveness and sustainability.
Methods and analysis
A systematic review (PROSPERO registration: CRD42022347871) was conducted by searching Embase, MEDLINE ALL, Web of Science and the Cochrane Central Register of Controlled Trials from inception to December 2023, supplemented by Google Scholar. English-language studies describing AI (machine learning, deep learning, predictive analytics) relevant to inpatient settings and referencing at least one WHO principle were included. Two reviewers independently screened titles, abstracts and full texts, extracting data on publication year, country, study design, AI type, technology readiness level and ethical considerations. Discrepancies were resolved by consensus.
Results
Of 4770 unique records, 673 were included. Most (83%) originated from high-income countries, with publication volume rising sharply after 2021. Of these, 558 (83%) addressed at least one WHO principle in depth, most frequently inclusiveness and equity (49%), transparency and explainability (45%) and autonomy (42%). Well-being and safety (26%) and responsibility and accountability (29%) were less frequently covered, while responsiveness and sustainability (6%) was rarely explored. Among 44 studies developing AI applications with technology readiness levels 1–6, ethical principles were acknowledged but rarely operationalised.
Conclusion
Hospital-based AI research demonstrates increasing attention to ethical principles but lacks comprehensive application, particularly regarding sustainability. High-income countries dominate this discourse, underscoring the need for broader global engagement. To achieve equitable, safe and sustainable AI in clinical practice, clearer operational guidance and more inclusive collaboration is warranted. ...
Objective
As hospitals increasingly adopt artificial intelligence (AI) to manage rising patient volumes, workforce shortages and healthcare costs, concerns about ethical implementation have become prominent. This systematic review aims to assess how hospital-focused AI literature addresses the WHO’s six ethical AI principles—autonomy; well-being and safety; transparency and explainability; responsibility and accountability; inclusiveness and equity; and responsiveness and sustainability.
Methods and analysis
A systematic review (PROSPERO registration: CRD42022347871) was conducted by searching Embase, MEDLINE ALL, Web of Science and the Cochrane Central Register of Controlled Trials from inception to December 2023, supplemented by Google Scholar. English-language studies describing AI (machine learning, deep learning, predictive analytics) relevant to inpatient settings and referencing at least one WHO principle were included. Two reviewers independently screened titles, abstracts and full texts, extracting data on publication year, country, study design, AI type, technology readiness level and ethical considerations. Discrepancies were resolved by consensus.
Results
Of 4770 unique records, 673 were included. Most (83%) originated from high-income countries, with publication volume rising sharply after 2021. Of these, 558 (83%) addressed at least one WHO principle in depth, most frequently inclusiveness and equity (49%), transparency and explainability (45%) and autonomy (42%). Well-being and safety (26%) and responsibility and accountability (29%) were less frequently covered, while responsiveness and sustainability (6%) was rarely explored. Among 44 studies developing AI applications with technology readiness levels 1–6, ethical principles were acknowledged but rarely operationalised.
Conclusion
Hospital-based AI research demonstrates increasing attention to ethical principles but lacks comprehensive application, particularly regarding sustainability. High-income countries dominate this discourse, underscoring the need for broader global engagement. To achieve equitable, safe and sustainable AI in clinical practice, clearer operational guidance and more inclusive collaboration is warranted.
As hospitals increasingly adopt artificial intelligence (AI) to manage rising patient volumes, workforce shortages and healthcare costs, concerns about ethical implementation have become prominent. This systematic review aims to assess how hospital-focused AI literature addresses the WHO’s six ethical AI principles—autonomy; well-being and safety; transparency and explainability; responsibility and accountability; inclusiveness and equity; and responsiveness and sustainability.
Methods and analysis
A systematic review (PROSPERO registration: CRD42022347871) was conducted by searching Embase, MEDLINE ALL, Web of Science and the Cochrane Central Register of Controlled Trials from inception to December 2023, supplemented by Google Scholar. English-language studies describing AI (machine learning, deep learning, predictive analytics) relevant to inpatient settings and referencing at least one WHO principle were included. Two reviewers independently screened titles, abstracts and full texts, extracting data on publication year, country, study design, AI type, technology readiness level and ethical considerations. Discrepancies were resolved by consensus.
Results
Of 4770 unique records, 673 were included. Most (83%) originated from high-income countries, with publication volume rising sharply after 2021. Of these, 558 (83%) addressed at least one WHO principle in depth, most frequently inclusiveness and equity (49%), transparency and explainability (45%) and autonomy (42%). Well-being and safety (26%) and responsibility and accountability (29%) were less frequently covered, while responsiveness and sustainability (6%) was rarely explored. Among 44 studies developing AI applications with technology readiness levels 1–6, ethical principles were acknowledged but rarely operationalised.
Conclusion
Hospital-based AI research demonstrates increasing attention to ethical principles but lacks comprehensive application, particularly regarding sustainability. High-income countries dominate this discourse, underscoring the need for broader global engagement. To achieve equitable, safe and sustainable AI in clinical practice, clearer operational guidance and more inclusive collaboration is warranted.
Journal article
(2025)
-
Denise E. Hilling, Imane Ihaddouchen, Stefan Buijsman, Reggie Townsend, Diederik Gommers, Michel E. van Genderen
Artificial Intelligence (AI) in healthcare holds transformative potential but faces critical challenges in ethical accountability and systemic inequities. Biases in AI models, such as lower diagnosis rates for Black women or gender stereotyping in Large Language Models, highlight the urgent need to address historical and structural inequalities in data and development processes. Disparities in clinical trials and datasets, often skewed toward high-income, English-speaking regions, amplify these issues. Moreover, the underrepresentation of marginalized groups among AI developers and researchers exacerbates these challenges. To ensure equitable AI, diverse data collection, federated data-sharing frameworks, and bias-correction techniques are essential. Structural initiatives, such as fairness audits, transparent AI model development processes, and early registration of clinical AI models, alongside inclusive global collaborations like TRAIN-Europe and CHAI, can drive responsible AI adoption. Prioritizing diversity in datasets and among developers and researchers, as well as implementing transparent governance will foster AI systems that uphold ethical principles and deliver equitable healthcare outcomes globally.
...
Artificial Intelligence (AI) in healthcare holds transformative potential but faces critical challenges in ethical accountability and systemic inequities. Biases in AI models, such as lower diagnosis rates for Black women or gender stereotyping in Large Language Models, highlight the urgent need to address historical and structural inequalities in data and development processes. Disparities in clinical trials and datasets, often skewed toward high-income, English-speaking regions, amplify these issues. Moreover, the underrepresentation of marginalized groups among AI developers and researchers exacerbates these challenges. To ensure equitable AI, diverse data collection, federated data-sharing frameworks, and bias-correction techniques are essential. Structural initiatives, such as fairness audits, transparent AI model development processes, and early registration of clinical AI models, alongside inclusive global collaborations like TRAIN-Europe and CHAI, can drive responsible AI adoption. Prioritizing diversity in datasets and among developers and researchers, as well as implementing transparent governance will foster AI systems that uphold ethical principles and deliver equitable healthcare outcomes globally.