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Michel van Genderen
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2 records found
1
The green ICU
How to interpret green? A multiple perspective approach
Journal article
(2025)
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Elisabeth Smale, Heather Baid, Nicole Hunfeld, Marko Balan, Forbes McGain, Scott McAlistar, Jan J. de Waele, Jan-Carel Diehl, Erik M. van Raaij, Michel van Genderen, Dick Tibboel
Mitigating environmental impacts is an urgent challenge supported by (scientific) intensive care societies worldwide. However, making green choices without compromising high-quality care for critically ill patients may be challenging. The current paper describes a three-step approach towards green intensive care units. Starting with the measurement of environmental sustainability, intensive care units can identify hotspots, quantify the environmental impacts of products and procedures, and monitor sustainable progress. Subsequently, a multidisciplinary approach is proposed to improve environmental sustainability, including a collaboration of procurement specialists and healthcare professionals, using co-creation and green teams as efficient grassroots change agents. A context-specific approach for enhancing sustainable healthcare practices is key in order to fit local regulatory requirements and create support of professionals. A final step is to share results and create momentum, including publishing initiatives and participating in online (inter)national networks. Based on the core sustainability principles, this three-step approach towards green ICUs provides a valuable tool to professionals worldwide to facilitate change towards environmentally responsible intensive care units.
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Mitigating environmental impacts is an urgent challenge supported by (scientific) intensive care societies worldwide. However, making green choices without compromising high-quality care for critically ill patients may be challenging. The current paper describes a three-step approach towards green intensive care units. Starting with the measurement of environmental sustainability, intensive care units can identify hotspots, quantify the environmental impacts of products and procedures, and monitor sustainable progress. Subsequently, a multidisciplinary approach is proposed to improve environmental sustainability, including a collaboration of procurement specialists and healthcare professionals, using co-creation and green teams as efficient grassroots change agents. A context-specific approach for enhancing sustainable healthcare practices is key in order to fit local regulatory requirements and create support of professionals. A final step is to share results and create momentum, including publishing initiatives and participating in online (inter)national networks. Based on the core sustainability principles, this three-step approach towards green ICUs provides a valuable tool to professionals worldwide to facilitate change towards environmentally responsible intensive care units.
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)
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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.