Clinicians must participate in the development of multimodal AI
Summary: Multimodal artificial intelligence (AI) is a powerful new technological advance, capable of simultaneously learning from diverse data types, such as text, images, video, and audio. Because clinical decisions are usually based on information from multiple sources, multimodal AI has the poten...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | EClinicalMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589537025001841 |
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| author | Christopher R.S. Banerji Aroon Bhardwaj Shah Ben Dabson Tapabrata Chakraborti Vicky Hellon Chris Harbron Ben D. MacArthur |
| author_facet | Christopher R.S. Banerji Aroon Bhardwaj Shah Ben Dabson Tapabrata Chakraborti Vicky Hellon Chris Harbron Ben D. MacArthur |
| author_sort | Christopher R.S. Banerji |
| collection | DOAJ |
| description | Summary: Multimodal artificial intelligence (AI) is a powerful new technological advance, capable of simultaneously learning from diverse data types, such as text, images, video, and audio. Because clinical decisions are usually based on information from multiple sources, multimodal AI has the potential to significantly improve clinical practice. However, unlike most developed multimodal AI workflows, clinical medicine is both a dynamic and interventional process in which the clinician continually learns about the patient's health and acts accordingly as data is collected. In this article we argue that multimodal clinical AI must be fully attuned to the particular challenges and constraints of the clinic, and clinician involvement is needed throughout development—not just at clinical deployment. We propose ways that clinician involvement can add value at each stage of the multimodal AI development pipeline, and argue for the establishment of actively managed multidisciplinary communities to work collaboratively towards the shared goal of improving the health of all. |
| format | Article |
| id | doaj-art-2b4ee7714a044f83b9917d99d0e4da16 |
| institution | OA Journals |
| issn | 2589-5370 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj-art-2b4ee7714a044f83b9917d99d0e4da162025-08-20T01:52:23ZengElsevierEClinicalMedicine2589-53702025-06-018410325210.1016/j.eclinm.2025.103252Clinicians must participate in the development of multimodal AIChristopher R.S. Banerji0Aroon Bhardwaj Shah1Ben Dabson2Tapabrata Chakraborti3Vicky Hellon4Chris Harbron5Ben D. MacArthur6The Alan Turing Institute, London, UK; University College London Hospital, University College London Hospitals NHS Trust, London, UK; UCL Cancer Institute, Faculty of Medical Sciences, University College London, London, UK; King's Comprehensive Cancer Centre, King's College London, London, UK; Corresponding author. The Alan Turing Institute, London, UK.Whittington Hospital, Whittington Health NHS Trust, London, UKHammersmith Hospital, Imperial College London NHS Trust, London, UKThe Alan Turing Institute, London, UK; UCL Cancer Institute, Faculty of Medical Sciences, University College London, London, UKThe Alan Turing Institute, London, UKRoche Pharmaceuticals, Welwyn Garden City, UKThe Alan Turing Institute, London, UK; Faculty of Medicine, University of Southampton, Southampton, UK; Mathematical Sciences, University of Southampton, Southampton, UK; Corresponding author. The Alan Turing Institute, London, UK.Summary: Multimodal artificial intelligence (AI) is a powerful new technological advance, capable of simultaneously learning from diverse data types, such as text, images, video, and audio. Because clinical decisions are usually based on information from multiple sources, multimodal AI has the potential to significantly improve clinical practice. However, unlike most developed multimodal AI workflows, clinical medicine is both a dynamic and interventional process in which the clinician continually learns about the patient's health and acts accordingly as data is collected. In this article we argue that multimodal clinical AI must be fully attuned to the particular challenges and constraints of the clinic, and clinician involvement is needed throughout development—not just at clinical deployment. We propose ways that clinician involvement can add value at each stage of the multimodal AI development pipeline, and argue for the establishment of actively managed multidisciplinary communities to work collaboratively towards the shared goal of improving the health of all.http://www.sciencedirect.com/science/article/pii/S2589537025001841Multimodal AIHuman-in-the-loop AIHealth policyCommunity managementClinical AI |
| spellingShingle | Christopher R.S. Banerji Aroon Bhardwaj Shah Ben Dabson Tapabrata Chakraborti Vicky Hellon Chris Harbron Ben D. MacArthur Clinicians must participate in the development of multimodal AI EClinicalMedicine Multimodal AI Human-in-the-loop AI Health policy Community management Clinical AI |
| title | Clinicians must participate in the development of multimodal AI |
| title_full | Clinicians must participate in the development of multimodal AI |
| title_fullStr | Clinicians must participate in the development of multimodal AI |
| title_full_unstemmed | Clinicians must participate in the development of multimodal AI |
| title_short | Clinicians must participate in the development of multimodal AI |
| title_sort | clinicians must participate in the development of multimodal ai |
| topic | Multimodal AI Human-in-the-loop AI Health policy Community management Clinical AI |
| url | http://www.sciencedirect.com/science/article/pii/S2589537025001841 |
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