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Artificial intelligence-based incident analysis and learning system to enhance patient safety and improve treatment quality – npj Digital Medicine
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Acknowledgements
This work was funded in part by the MSKCC Support Grant P30 CA008748. We acknowledge the support of the High-Performance Computing Group in the Department of Digital Informatics & Technology Solutions at MSKCC for providing the computing infrastructure and resources necessary for this project. We also extend our gratitude to Esther Rulnick from the Division of Quality and Safety at MSKCC for their assistance in extracting incidents from the RISQ database.
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Competing interests
The authors declare no competing interests related to this work. EH declares the following disclosures: Volunteering position on the executive board of the Knickerbocker Chapter NSDAR. J.M.M. declares the following disclosures: Grants from Varian received during my time at the University of Michigan. Honoraria paid by the Connecticut Area Medical Physics Society. Patient issued for Combined radiation acoustics and ultrasound for radiotherapy guidance and cancer targeting. Co-founder and board member at Prexient, Inc. Chair of Work Group on Science Council EDI at the American Association of Physicists in Medicine. Chair of Work Group on Report Writing at the American Association of Physicists in Medicine. Vice Chair of Research Committee at the Radiation Oncology Institute. Co-Chair at the Radiation Oncology Institute Radiation Oncology Safety Stakeholders Initiative. FuseOncology (Copyright), which has been licensed by my previous institution (University of Michigan) for which I made a contribution. Consultant (unfunded) to the Michigan Radiation Oncology Quality Consortium.
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Jinia, A.J., Chapman, K., Liu, S. et al. Artificial intelligence-based incident analysis and learning system to enhance patient safety and improve treatment quality. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02390-2
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DOI: https://doi.org/10.1038/s41746-026-02390-2
