General learnings from the horizon 2020 project BigMedilytics

Roland Roller; Supriyo Chatterjea; Brian Pickering; Holmer Hemsen; Dimitrios Vogiatzis; Ricard Martínez Martínez; Georg Langs; Simona Rabinovici-Cohen; Wiebke Duettmann; Alex Sangers; Maria Esther Vidal; Ernestina Menasalvas Ruiz; Marga Martin Sanchez; Josep Redon; Ana Ferrer-Albero; Alexandra Muñoz-Oliver; Gerrit J. Noordergraaf; Igor Paulussen; Per Henrik Vincent; Arne Ijpma; José Ramón Navarro-Cerdán; Santiago Gálvez-Settier

doi:10.1561/9781638282372.ch27

General learnings from the horizon 2020 project BigMedilytics

Roland Roller^*, Supriyo Chatterjea, Brian Pickering, Holmer Hemsen, Dimitrios Vogiatzis, Ricard Martínez Martínez, Georg Langs, Simona Rabinovici-Cohen, Wiebke Duettmann, Alex Sangers, Maria Esther Vidal, Ernestina Menasalvas Ruiz, Marga Martin Sanchez, Josep Redon, Ana Ferrer-Albero, Alexandra Muñoz-Oliver, Gerrit J. Noordergraaf, Igor Paulussen, Per Henrik Vincent, Arne IjpmaJosé Ramón Navarro-Cerdán, Santiago Gálvez-Settier

^*Corresponding author for this work

Pathology

Research output: Chapter/Conference proceeding › Chapter › Academic

Abstract

Big Data, in combination with Artificial Intelligence (AI), has the potential to change and improve processes in medicine. However, these activities/technologies must be developed to promote the trust of all stakeholders: patients, healthcare professionals, private and public providers, and businesses. Providing a trustworthy AI – lawful, ethical, and robust – requires significant efforts. Although technological development is moving quickly, testing, validation, and integration of such innovation may take many years. The reasons that slow down this process are manifold. However, some barriers and pitfalls are foreseeable and, therefore, can be taken into account or avoided. In order to support future development and integration of AI and BigData technologies, we present technical challenges and lessons learned from our previous project, BigMedilytics, involving clinicians and data scientists. This chapter considers the challenges data scientists providing advanced technology in the healthcare domain may face, along with some suggestions to address any related issues if applicable.

Original language	English
Title of host publication	Technology in Healthcare
Subtitle of host publication	Introduction, Clinical Impacts, Workflow Improvement, Structuring and Assessment
Chapter	27
Pages	341-360
Number of pages	21
ISBN (Electronic)	9781638282372
DOIs	https://doi.org/10.1561/9781638282372.ch27
Publication status	Published - 8 Jul 2024

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Roller, R., Chatterjea, S., Pickering, B., Hemsen, H., Vogiatzis, D., Martínez, R. M., Langs, G., Rabinovici-Cohen, S., Duettmann, W., Sangers, A., Vidal, M. E., Ruiz, E. M., Sanchez, M. M., Redon, J., Ferrer-Albero, A., Muñoz-Oliver, A., Noordergraaf, G. J., Paulussen, I., Vincent, P. H., ... Gálvez-Settier, S. (2024). General learnings from the horizon 2020 project BigMedilytics. In Technology in Healthcare: Introduction, Clinical Impacts, Workflow Improvement, Structuring and Assessment (pp. 341-360) https://doi.org/10.1561/9781638282372.ch27

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abstract = "Big Data, in combination with Artificial Intelligence (AI), has the potential to change and improve processes in medicine. However, these activities/technologies must be developed to promote the trust of all stakeholders: patients, healthcare professionals, private and public providers, and businesses. Providing a trustworthy AI – lawful, ethical, and robust – requires significant efforts. Although technological development is moving quickly, testing, validation, and integration of such innovation may take many years. The reasons that slow down this process are manifold. However, some barriers and pitfalls are foreseeable and, therefore, can be taken into account or avoided. In order to support future development and integration of AI and BigData technologies, we present technical challenges and lessons learned from our previous project, BigMedilytics, involving clinicians and data scientists. This chapter considers the challenges data scientists providing advanced technology in the healthcare domain may face, along with some suggestions to address any related issues if applicable.",

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Roller, R, Chatterjea, S, Pickering, B, Hemsen, H, Vogiatzis, D, Martínez, RM, Langs, G, Rabinovici-Cohen, S, Duettmann, W, Sangers, A, Vidal, ME, Ruiz, EM, Sanchez, MM, Redon, J, Ferrer-Albero, A, Muñoz-Oliver, A, Noordergraaf, GJ, Paulussen, I, Vincent, PH, Ijpma, A, Navarro-Cerdán, JR & Gálvez-Settier, S 2024, General learnings from the horizon 2020 project BigMedilytics. in Technology in Healthcare: Introduction, Clinical Impacts, Workflow Improvement, Structuring and Assessment. pp. 341-360. https://doi.org/10.1561/9781638282372.ch27

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AU - Roller, Roland

AU - Chatterjea, Supriyo

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AU - Hemsen, Holmer

AU - Vogiatzis, Dimitrios

AU - Martínez, Ricard Martínez

AU - Langs, Georg

AU - Rabinovici-Cohen, Simona

AU - Duettmann, Wiebke

AU - Sangers, Alex

AU - Vidal, Maria Esther

AU - Ruiz, Ernestina Menasalvas

AU - Sanchez, Marga Martin

AU - Redon, Josep

AU - Ferrer-Albero, Ana

AU - Muñoz-Oliver, Alexandra

AU - Noordergraaf, Gerrit J.

AU - Paulussen, Igor

AU - Vincent, Per Henrik

AU - Ijpma, Arne

AU - Navarro-Cerdán, José Ramón

AU - Gálvez-Settier, Santiago

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AB - Big Data, in combination with Artificial Intelligence (AI), has the potential to change and improve processes in medicine. However, these activities/technologies must be developed to promote the trust of all stakeholders: patients, healthcare professionals, private and public providers, and businesses. Providing a trustworthy AI – lawful, ethical, and robust – requires significant efforts. Although technological development is moving quickly, testing, validation, and integration of such innovation may take many years. The reasons that slow down this process are manifold. However, some barriers and pitfalls are foreseeable and, therefore, can be taken into account or avoided. In order to support future development and integration of AI and BigData technologies, we present technical challenges and lessons learned from our previous project, BigMedilytics, involving clinicians and data scientists. This chapter considers the challenges data scientists providing advanced technology in the healthcare domain may face, along with some suggestions to address any related issues if applicable.

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General learnings from the horizon 2020 project BigMedilytics

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