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Chng SY, Tern MJW, Lee YS, Cheng LTE, Kapur J, Eriksson JG, Chong YS, Savulescu J. Ethical considerations in AI for child health and recommendations for child-centered medical AI. NPJ Digit Med 2025; 8:152. [PMID: 40065130 PMCID: PMC11893894 DOI: 10.1038/s41746-025-01541-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 02/26/2025] [Indexed: 03/14/2025] Open
Abstract
There does not exist any previous comprehensive review on AI ethics in child health or any guidelines for management, unlike in adult medicine. This review describes ethical principles in AI for child health and provides recommendations for child-centered medical AI. We also introduce the Pediatrics EthicAl Recommendations List for AI (PEARL-AI) framework for clinicians and AI developers to ensure ethical AI enabled systems in healthcare for children.
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Affiliation(s)
- Seo Yi Chng
- Krsyma Medical AI Pte Ltd, Singapore, Singapore.
| | | | - Yung Seng Lee
- Department of Paediatrics, National University of Singapore, Singapore, Singapore
| | - Lionel Tim-Ee Cheng
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Jeevesh Kapur
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Johan Gunnar Eriksson
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗ STAR), Singapore, Singapore
- Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore, Singapore
- Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗ STAR), Singapore, Singapore
- Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore, Singapore
| | - Julian Savulescu
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Biomedical Research Group, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, UK
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Lekadir K, Frangi AF, Porras AR, Glocker B, Cintas C, Langlotz CP, Weicken E, Asselbergs FW, Prior F, Collins GS, Kaissis G, Tsakou G, Buvat I, Kalpathy-Cramer J, Mongan J, Schnabel JA, Kushibar K, Riklund K, Marias K, Amugongo LM, Fromont LA, Maier-Hein L, Cerdá-Alberich L, Martí-Bonmatí L, Cardoso MJ, Bobowicz M, Shabani M, Tsiknakis M, Zuluaga MA, Fritzsche MC, Camacho M, Linguraru MG, Wenzel M, De Bruijne M, Tolsgaard MG, Goisauf M, Cano Abadía M, Papanikolaou N, Lazrak N, Pujol O, Osuala R, Napel S, Colantonio S, Joshi S, Klein S, Aussó S, Rogers WA, Salahuddin Z, Starmans MPA. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 2025; 388:e081554. [PMID: 39909534 PMCID: PMC11795397 DOI: 10.1136/bmj-2024-081554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/10/2025] [Indexed: 02/07/2025]
Affiliation(s)
- Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Alejandro F Frangi
- Center for Computational Imaging & Simulation Technologies in Biomedicine, Schools of Computing and Medicine, University of Leeds, Leeds, UK
- Medical Imaging Research Centre (MIRC), Cardiovascular Science and Electronic Engineering Departments, KU Leuven, Leuven, Belgium
| | - Antonio R Porras
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | | | - Curtis P Langlotz
- Departments of Radiology, Medicine, and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Eva Weicken
- Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Folkert W Asselbergs
- Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Gary S Collins
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Georgios Kaissis
- Institute for AI and Informatics in Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Gianna Tsakou
- Gruppo Maggioli, Research and Development Lab, Athens, Greece
| | | | | | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Julia A Schnabel
- Institute of Machine Learning in Biomedical Imaging, Helmholtz Center Munich, Munich, Germany
| | - Kaisar Kushibar
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Katrine Riklund
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - Kostas Marias
- Foundation for Research and Technology-Hellas (FORTH), Crete, Greece
| | - Lameck M Amugongo
- Department of Software Engineering, Namibia University of Science & Technology, Windhoek, Namibia
| | - Lauren A Fromont
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Lena Maier-Hein
- Division of Intelligent Medical Systems, German Cancer Research Centre, Heidelberg, Germany
| | | | - Luis Martí-Bonmatí
- Medical Imaging Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - M Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Maciej Bobowicz
- 2nd Division of Radiology, Medical University of Gdansk, Gdansk, Poland
| | - Mahsa Shabani
- Faculty of Law and Criminology, Ghent University, Ghent, Belgium
| | - Manolis Tsiknakis
- Foundation for Research and Technology-Hellas (FORTH), Crete, Greece
| | | | | | - Marina Camacho
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA
| | - Markus Wenzel
- Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Marleen De Bruijne
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Centre, Rotterdam, Netherlands
| | - Martin G Tolsgaard
- Copenhagen Academy for Medical Education and Simulation Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | | | | | | | - Noussair Lazrak
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Oriol Pujol
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Richard Osuala
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Sandy Napel
- Integrative Biomedical Imaging Informatics at Stanford (IBIIS), Department of Radiology, Stanford University, Stanford, CA, USA
| | - Sara Colantonio
- Institute of Information Science and Technologies of the National Research Council of Italy, Pisa, Italy
| | - Smriti Joshi
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Stefan Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Centre, Rotterdam, Netherlands
| | - Susanna Aussó
- Artificial Intelligence in Healthcare Program, TIC Salut Social Foundation, Barcelona, Spain
| | - Wendy A Rogers
- Department of Philosophy, and School of Medicine, Macquarie University, Sydney, Australia
| | - Zohaib Salahuddin
- The D-lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Martijn P A Starmans
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Centre, Rotterdam, Netherlands
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Miskeen E, Alfaifi J, Alhuian DM, Alghamdi M, Alharthi MH, Alshahrani NA, Alosaimi G, Alshomrani RA, Hajlaa AM, Khair NM, Almuawi AM, Al-Jaber KH, Elrasheed FE, Elhassan K, Abbas M. Prospective Applications of Artificial Intelligence In Fetal Medicine: A Scoping Review of Recent Updates. Int J Gen Med 2025; 18:237-245. [PMID: 39834911 PMCID: PMC11745059 DOI: 10.2147/ijgm.s490261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Accepted: 01/08/2025] [Indexed: 01/22/2025] Open
Abstract
Introduction With the incorporation of artificial intelligence (AI), significant advancements have occurred in the field of fetal medicine, holding the potential to transform prenatal care and diagnostics, promising to revolutionize prenatal care and diagnostics. This scoping review aims to explore the recent updates in the prospective application of AI in fetal medicine, evaluating its current uses, potential benefits, and limitations. Methods Compiling literature concerning the utilization of AI in fetal medicine does not appear to modify the subject or provide an exhaustive exploration of electronic databases. Relevant studies, reviews, and articles published in recent years were incorporated to ensure up-to-date data. The selected works were analyzed for common themes, AI methodologies applied, and the scope of AI's integration into fetal medicine practice. Results The review identified several key areas where AI applications are making strides in fetal medicine, including prenatal screening, diagnosis of congenital anomalies, and predicting pregnancy complications. AI-driven algorithms have been developed to analyze complex fetal ultrasound data, enhancing image quality and interpretative accuracy. The integration of AI in fetal monitoring has also been explored, with systems designed to identify patterns indicative of fetal distress. Despite these advancements, challenges related to the ethical use of AI, data privacy, and the need for extensive validation of AI tools in diverse populations were noted. Conclusion The potential benefits of AI in fetal medicine are immense, offering a brighter future for our field. AI equips us with tools for enhanced diagnosis, monitoring, and prognostic capabilities, promising to revolutionize the way we approach prenatal care and diagnostics. This optimistic outlook underscores the need for further research and interdisciplinary partnerships to fully leverage AI's potential in driving forward the practice of fetal medicine.
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Affiliation(s)
- Elhadi Miskeen
- Department of Obstetrics and Gynecology, College of Medicine, University of Bisha, Bisha, Saudi Arabia
| | - Jaber Alfaifi
- Department of Child Health, College of Medicine University of Bisha, Bisha, Saudi Arabia
| | | | - Mushabab Alghamdi
- Department of Internal Medicine, College of Medicine, University of Bisha, Bisha, Saudi Arabia
| | - Muffarah Hamid Alharthi
- Department of Family and Community Medicine, College of Medicine, University of Bisha, Bisha, Saudi Arabia
| | | | - Ghala Alosaimi
- Medical student, College of Medicine, Taif University, Taif, Saudi Arabia
| | | | | | | | | | | | - Fath Elrahman Elrasheed
- Department of Obstetrics and Gynecology, Faculty of Medicine Najran University, Najran, Saudi Arabia
| | - Kamal Elhassan
- Department of Family and Community Medicine, College of Medicine, University of Bisha, Bisha, Saudi Arabia
| | - Mohammed Abbas
- Department of Pediatrics, College of Medicine, Arab Gulf University, Al Manama, Bahrain
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Haber Y, Levkovich I, Hadar-Shoval D, Elyoseph Z. The Artificial Third: A Broad View of the Effects of Introducing Generative Artificial Intelligence on Psychotherapy. JMIR Ment Health 2024; 11:e54781. [PMID: 38787297 PMCID: PMC11137430 DOI: 10.2196/54781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/24/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
Unlabelled This paper explores a significant shift in the field of mental health in general and psychotherapy in particular following generative artificial intelligence's new capabilities in processing and generating humanlike language. Following Freud, this lingo-technological development is conceptualized as the "fourth narcissistic blow" that science inflicts on humanity. We argue that this narcissistic blow has a potentially dramatic influence on perceptions of human society, interrelationships, and the self. We should, accordingly, expect dramatic changes in perceptions of the therapeutic act following the emergence of what we term the artificial third in the field of psychotherapy. The introduction of an artificial third marks a critical juncture, prompting us to ask the following important core questions that address two basic elements of critical thinking, namely, transparency and autonomy: (1) What is this new artificial presence in therapy relationships? (2) How does it reshape our perception of ourselves and our interpersonal dynamics? and (3) What remains of the irreplaceable human elements at the core of therapy? Given the ethical implications that arise from these questions, this paper proposes that the artificial third can be a valuable asset when applied with insight and ethical consideration, enhancing but not replacing the human touch in therapy.
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Affiliation(s)
- Yuval Haber
- The PhD Program of Hermeneutics and Cultural Studies, Interdisciplinary Studies Unit, Bar-Ilan University, Ramat Gan, Israel
| | | | - Dorit Hadar-Shoval
- Department of Psychology and Educational Counseling, The Max Stern Yezreel Valley College, Emek Yezreel, Israel
| | - Zohar Elyoseph
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
- The Center for Psychobiological Research, Department of Psychology and Educational Counseling, The Max Stern Yezreel Valley College, Emek Yezreel, Israel
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Berghea EC, Ionescu MD, Gheorghiu RM, Tincu IF, Cobilinschi CO, Craiu M, Bălgrădean M, Berghea F. Integrating Artificial Intelligence in Pediatric Healthcare: Parental Perceptions and Ethical Implications. CHILDREN (BASEL, SWITZERLAND) 2024; 11:240. [PMID: 38397353 PMCID: PMC10887612 DOI: 10.3390/children11020240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/30/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND Our study aimed to explore the way artificial intelligence (AI) utilization is perceived in pediatric medicine, examining its acceptance among patients (in this case represented by their adult parents), and identify the challenges it presents in order to understand the factors influencing its adoption in clinical settings. METHODS A structured questionnaire was applied to caregivers (parents or grandparents) of children who presented in tertiary pediatric clinics. RESULTS The most significant differentiations were identified in relation to the level of education (e.g., aversion to AI involvement was 22.2% among those with postgraduate degrees, 43.9% among those with university degrees, and 54.5% among those who only completed high school). The greatest fear among respondents regarding the medical use of AI was related to the possibility of errors occurring (70.1%). CONCLUSIONS The general attitude toward the use of AI can be considered positive, provided that it remains human-supervised, and that the technology used is explained in detail by the physician. However, there were large differences among groups (mainly defined by education level) in the way AI is perceived and accepted.
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Affiliation(s)
- Elena Camelia Berghea
- “Marie S. Curie” Emergency Children’s Clinical Hospital, Carol Davila University of Medicine and Pharmacy, 041451 Bucharest, Romania; (E.C.B.); (M.B.)
| | - Marcela Daniela Ionescu
- “Marie S. Curie” Emergency Children’s Clinical Hospital, Carol Davila University of Medicine and Pharmacy, 041451 Bucharest, Romania; (E.C.B.); (M.B.)
| | - Radu Marian Gheorghiu
- National Institute for Mother and Child Health “Alessandrescu-Rusescu”, Carol Davila University of Medicine and Pharmacy, 041249 Bucharest, Romania;
| | - Iulia Florentina Tincu
- Dr. Victor Gomoiu Clinical Children Hospital, Carol Davila University of Medicine and Pharmacy, 022102 Bucharest, Romania;
| | - Claudia Oana Cobilinschi
- Sfanta Maria Clinica Hospital, Carol Davila University of Medicine and Pharmacy, 011172 Bucharest, Romania; (C.O.C.); (F.B.)
| | - Mihai Craiu
- National Institute for Mother and Child Health “Alessandrescu-Rusescu”, Carol Davila University of Medicine and Pharmacy, 041249 Bucharest, Romania;
| | - Mihaela Bălgrădean
- “Marie S. Curie” Emergency Children’s Clinical Hospital, Carol Davila University of Medicine and Pharmacy, 041451 Bucharest, Romania; (E.C.B.); (M.B.)
| | - Florian Berghea
- Sfanta Maria Clinica Hospital, Carol Davila University of Medicine and Pharmacy, 011172 Bucharest, Romania; (C.O.C.); (F.B.)
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