Crowdsourcing AI solutions in healthcare using sensitive data in accordance with regulatory guidelines for translational medicine
Abstract
The uptake of new digital and AI based technologies in the healthcare setting, including digital diagnostics, has been comparatively slower than in other sectors. This is a consequence of the complex legislature, and the lack of routine procedures for the secondary use of sensitive medical data. Inadequate level of education and incentives for different stakeholders prevents overcoming strong barriers, such as clinical workflow integration, and need for comprehensive evaluation of utility of interventions which requires multi-disciplinary collaboration effort. The paper presents a re-hospitalization challenge, organised by the European digital innovation hub (EDIH) - AI4Health.Cro, a co-creation activity and an effective tool for overcoming the mentioned barriers for implementation of new AI technologies and overall innovation ecosystem maturation. Crowdsourcing challenges or datathons are R&D strategies where ad-hoc assembled competing teams collaborate on early-stage technology development or research problem solving. Our re-hospitalisation challenge has had multiple facets: as activity for construction of an AI predictive model with explanatory support and plausible business model for its deployment; it involved also extensive organisers effort: data anonymisation, provision of secure processing environment, and comprehensive assessment of solutions. The paper details the challenge structure and processes, from problem inception to execution and evaluation. As a co-creation effort in digital healthcare, the challenge provided results on multiple levels: (i) tailored solutions based on primary data, serving as prototypes for later deployment in practice; (ii) sandboxing EHDS principles to enable secondary data use; (iii) ecosystem building event yielding cross-disciplinary participative learning experience. Innovation challenges arent just competitions; they are a building tool that provides several direct and indirect impacts, from developing healthcare technologies, supporting startups, to streamlining secondary use of clinical data for research and innovation and eliciting regulatory needs. Raising the awareness of all relevant stakeholders about the translation of AI technologies into routine clinical setting.
Author summaryHealthcare has been slower than other sectors to adopt new digital and AI-based technologies. This slow progress largely reflects the complexity of EU legislation and the lack of practical procedures that translate legal requirements into guidance for stakeholders using sensitive medical data in research and innovation. A crowdsourcing pilot to predict likelihood of early rehospitalisation using AI methodology provided opportunity for hands-on experience for experts from clinical, computational and business background on both synthetic and real-world medical data. Together we clarify legal responsibilities and data access conditions, and facilitate collaboration of relevant stakeholders, clinical, technical, public and research entities for ecosystem building. We provide the first Croatian European Health Data Space compliant secure processing environment, created for future use by researchers and innovators. Methodologically varied AI based prototypes were developed to predict early rehospitalization carefully balancing data overfitting, AI explainability and market potential of technology, in turn raising multidisciplinary skills, and awareness of conditions required for innovation to thrive in 28 participating teams. This type of competition, provided within European Digital Innovation Hub is an ideal platform to build regulatory sandboxes and pilot novel legislative platforms ahead of their full-scale implementation.
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BibTeX
@misc{keber2026crowdsourcing,
title = {{Crowdsourcing AI solutions in healthcare using sensitive data in accordance with regulatory guidelines for translational medicine}},
author = {M. Keber and M. Bagic and I. Kulikovskikh and M. Piskorec and P. Ivanko and D. Orsolic and M. Projic and Z. Barisic and A. Jeroncic and N. Sesto and M. Sams-Bival and S. Koscina and Z. Potocanac and A. Stajduhar and J. Kolic and K. Pintaric and L. Trgovec-Greif and M. Trgovec-Greif and I. Buljan and R. Sango and F. Miocinovic and F. Đerke and Z. Antolovic and M. Pavlovic and T. Smuc and A. Baresic},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.10.26362629},
url = {https://www.medrxiv.org/content/10.64898/2026.09.10.26362629v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Crowdsourcing AI solutions in healthcare using sensitive data in accordance with regulatory guidelines for translational medicine
AU - M. Keber
AU - M. Bagic
AU - I. Kulikovskikh
AU - M. Piskorec
AU - P. Ivanko
AU - D. Orsolic
AU - M. Projic
AU - Z. Barisic
AU - A. Jeroncic
AU - N. Sesto
AU - M. Sams-Bival
AU - S. Koscina
AU - Z. Potocanac
AU - A. Stajduhar
AU - J. Kolic
AU - K. Pintaric
AU - L. Trgovec-Greif
AU - M. Trgovec-Greif
AU - I. Buljan
AU - R. Sango
AU - F. Miocinovic
AU - F. Đerke
AU - Z. Antolovic
AU - M. Pavlovic
AU - T. Smuc
AU - A. Baresic
PY - 2026
DA - 2026/09/10
PB - medRxiv
DO - 10.64898/2026.09.10.26362629
UR - https://www.medrxiv.org/content/10.64898/2026.09.10.26362629v1
AB - The uptake of new digital and AI based technologies in the healthcare setting, including digital diagnostics, has been comparatively slower than in other sectors. This is a consequence of the complex legislature, and the lack of routine procedures for the secondary use of sensitive medical data. Inadequate level of education and incentives for different stakeholders prevents overcoming strong barriers, such as clinical workflow integration, and need for comprehensive evaluation of utility of interventions which requires multi-disciplinary collaboration effort. The paper presents a re-hospitalization challenge, organised by the European digital innovation hub (EDIH) - AI4Health.Cro, a co-creation activity and an effective tool for overcoming the mentioned barriers for implementation of new AI technologies and overall innovation ecosystem maturation. Crowdsourcing challenges or datathons are R&D strategies where ad-hoc assembled competing teams collaborate on early-stage technology development or research problem solving. Our re-hospitalisation challenge has had multiple facets: as activity for construction of an AI predictive model with explanatory support and plausible business model for its deployment; it involved also extensive organisers effort: data anonymisation, provision of secure processing environment, and comprehensive assessment of solutions. The paper details the challenge structure and processes, from problem inception to execution and evaluation. As a co-creation effort in digital healthcare, the challenge provided results on multiple levels: (i) tailored solutions based on primary data, serving as prototypes for later deployment in practice; (ii) sandboxing EHDS principles to enable secondary data use; (iii) ecosystem building event yielding cross-disciplinary participative learning experience. Innovation challenges arent just competitions; they are a building tool that provides several direct and indirect impacts, from developing healthcare technologies, supporting startups, to streamlining secondary use of clinical data for research and innovation and eliciting regulatory needs. Raising the awareness of all relevant stakeholders about the translation of AI technologies into routine clinical setting. Author summaryHealthcare has been slower than other sectors to adopt new digital and AI-based technologies. This slow progress largely reflects the complexity of EU legislation and the lack of practical procedures that translate legal requirements into guidance for stakeholders using sensitive medical data in research and innovation. A crowdsourcing pilot to predict likelihood of early rehospitalisation using AI methodology provided opportunity for hands-on experience for experts from clinical, computational and business background on both synthetic and real-world medical data. Together we clarify legal responsibilities and data access conditions, and facilitate collaboration of relevant stakeholders, clinical, technical, public and research entities for ecosystem building. We provide the first Croatian European Health Data Space compliant secure processing environment, created for future use by researchers and innovators. Methodologically varied AI based prototypes were developed to predict early rehospitalization carefully balancing data overfitting, AI explainability and market potential of technology, in turn raising multidisciplinary skills, and awareness of conditions required for innovation to thrive in 28 participating teams. This type of competition, provided within European Digital Innovation Hub is an ideal platform to build regulatory sandboxes and pilot novel legislative platforms ahead of their full-scale implementation.
ER -