{
  "abstract": "Objectives To describe the implementation of a multidisciplinary, ethically grounded hackathon as a model to develop and evaluate generative AI (GenAI) solutions for real-world clinical challenges within a hospital setting.Methods The GenAI Health Hackathon (GAHH) organised at Hospital Clínic de Barcelona included 13 challenges were selected via an internal call based on clinical impact, feasibility and data availability. Participants accessed anonymised real-world data through a secure cloud environment. Teams employed large language models and retrieval-augmented generation to build prototypes addressing tasks such as clinical text structuring, decision support and workflow automation. Human-in-the-loop validation, explainability and regulatory safeguards were emphasised.Results The hackathon yielded multiple AI prototypes tested on real data. Results varied: entity recognition reached 90.5% accuracy, summarisation >90% clinician concordance and nutritional models achieved F1 scores of 0.75–0.93. Lower scores (F1<0.52, Jaccard Index <0.4) were seen in complex reasoning or multilingual tasks. Bias was explored in 10 projects, with mitigations such as stratified sampling, prompt tuning, disclaimers and expert oversight. A transferable framework was proposed to replicate responsible GenAI hackathons in clinical contexts.Discussion Interdisciplinary collaboration and real-world testing proved essential for aligning GenAI with clinical needs. The hackathon revealed challenges in bias, evaluation and integration but offered a transferable framework for responsible innovation under General Data Protection Regulation and the European Union Artificial Intelligence Act.Conclusions The GAHH demonstrated that GenAI can be safely and effectively applied in healthcare with rigorous governance and interdisciplinary collaboration, offering a scalable model for responsible AI innovation.",
  "authors": [
    {
      "affiliations": [
        "Clinical Informatics Service, Hospital Clínic de Barcelona, Barcelona, Spain",
        "Clinical Foundations, Universitat de Barcelona Facultat de Medicina i Ciencies de la Salut, Barcelona, Spain",
        "Multimodal Imaging Biomarkers, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain"
      ],
      "name": "Santiago Frid"
    },
    {
      "affiliations": [
        "Liver Intensive Care, Liver Unit, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Octavi Bassegoda"
    },
    {
      "affiliations": [
        "Department of Strategy and Planning, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Maria Araceli Camacho Mahamud"
    },
    {
      "affiliations": [
        "IT Department, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Gemma Sanjuan"
    },
    {
      "affiliations": [
        "Big Data Department, PMC-FPS, Regional Government of Andalusia Ministry of Health and Consumer Affairs, Seville, Spain"
      ],
      "name": "Miguel Ángel Armengol de la Hoz"
    },
    {
      "affiliations": [
        "Division of Health Sciences and Tech, Massachusetts Institute of Technology, Boston, Massachusetts, USA"
      ],
      "name": "Leo Celi"
    },
    {
      "affiliations": [
        "Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Barcelona, Spain"
      ],
      "name": "Isaac Cano Franco"
    },
    {
      "affiliations": [
        "Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain",
        "Bipolar and Depressive Disorders Unit, Digital Innovation Group, IDIBAPS, Barcelona, Spain"
      ],
      "name": "Gerard Anmella"
    },
    {
      "affiliations": [
        "Anesthesiology Service, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Tomas Cuñat López"
    },
    {
      "affiliations": [
        "Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Barcelona, Spain",
        "Department of Clinical Pharmacology, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Ana Lucía Arellano"
    },
    {
      "affiliations": [
        "Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Barcelona, Spain",
        "Department of Clinical Pharmacology, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Lina María Leguízamo-Martínez"
    },
    {
      "affiliations": [
        "Medical Oncology Service, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Laura Mezquita"
    },
    {
      "affiliations": [
        "Clinical Informatics Service, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Petter Axcell Peñafiel Macías"
    },
    {
      "affiliations": [
        "Infectious Diseases Service, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Antonio Gallardo-Pizarro"
    },
    {
      "affiliations": [
        "Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Barcelona, Spain"
      ],
      "name": "Ruben González Colom"
    },
    {
      "affiliations": [
        "Stroke Unit, Hospital Clínic de Barcelona, Barcelona, Spain"
      ],
      "name": "Arturo Renú Jornet"
    },
    {
      "affiliations": [
        "Clinical Informatics Service, Hospital Clínic de Barcelona, Barcelona, Spain",
        "Faculty of Mathematics and Statistics, Universitat Politecnica de Catalunya, Barcelona, Spain"
      ],
      "name": "Guillem Bracons Cucó"
    },
    {
      "affiliations": [
        "Clinical Informatics, Hospital Clínic de Barcelona, Barcelona, Spain",
        "Clinical Foundations Department, Universitat de Barcelona, Barcelona, Spain"
      ],
      "name": "Xavier Borrat Frigola"
    }
  ],
  "title": "Bridging generative AI and healthcare practice: insights from the GenAI Health Hackathon at Hospital Clínic de Barcelona",
  "uid": "593b23f9-4aee-5c1d-b961-988af99d9ce0"
}