Mandl, Kenneth D.

SMART on FHIR: a standards-based, interoperable apps platform for electronic health records

Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsNursing Diagnosis and Documentation

Cited by 829

Developing and adopting safe and effective digital biomarkers to improve patient outcomes

Coravos, A., Khozin, S., & Mandl, K. D. (2019) · npj Digital Medicine

Mobile Health and mHealth ApplicationsHealthcare Technology and Patient Monitoring

Cited by 361

Availability and quality of mobile health app privacy policies

Sunyaev, A., Dehling, T., Taylor, P. L., & Mandl, K. D. (2015) · Journal of the American Medical Informatics Association

Privacy, Security, and Data ProtectionMobile Health and mHealth Applications

Cited by 360

Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners’ Views

Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019) · Journal of Medical Internet Research

Artificial Intelligence in Healthcare and EducationPrimary Care and Health Outcomes

Cited by 346

Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency

Cutillo, C. M., Sharma, K. R., Foschini, L., Kundu, S., Mackintosh, M., Mandl, K. D., MI in Healthcare Workshop Working Group, Beck, T., Collier, E., Colvis, C., et al. (2020) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 337

Outcome Reporting Among Drug Trials Registered in ClinicalTrials.gov

Bourgeois, F. T., Murthy, S., & Mandl, K. D. (2010) · Annals of Internal Medicine

Pharmaceutical industry and healthcareStatistical Methods in Clinical Trials

Cited by 332

Digitizing clinical trials

Inan, O. T., Tenaerts, P., Prindiville, S. A., Reynolds, H. R., Dizon, D. S., Cooper-Arnold, K., Turakhia, M., Pletcher, M. J., Preston, K. L., Krumholz, H. M., et al. (2020) · npj Digital Medicine

Mobile Health and mHealth ApplicationsStatistical Methods in Clinical Trials

Cited by 329

Electronic Patient-Physician Communication: Problems and Promise

Mandl, K. D., Kohane, I. S., & Brandt, A. M. (1998) · Annals of Internal Medicine

Electronic Health Records SystemsTelemedicine and Telehealth Implementation

Cited by 222

AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence

Angus, D. C., Khera, R., Lieu, T., Liu, V., Ahmad, F. S., Anderson, B., Bhavani, S. V., Bindman, A., Brennan, T., Celi, L. A., et al. (2025) · JAMA

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 195

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Kohane, I. S., Aronow, B. J., Avillach, P., Beaulieu-Jones, B. K., Bellazzi, R., Bradford, R. L., Brat, G. A., Cannataro, M., Cimino, J. J., García-Barrio, N., et al. (2021) · Journal of Medical Internet Research

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 113

Biases introduced by filtering electronic health records for patients with “complete data.”

Weber, G. M., Adams, W. G., Bernstam, E. V., Bickel, J. P., Fox, K. P., Marsolo, K., Raghavan, V. A., Turchin, A., Zhou, X., Murphy, S. N., et al. (2017) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 93

Association of Sex With Recurrence of Autism Spectrum Disorder Among Siblings

Palmer, N., Beam, A., Agniel, D., Eran, A., Manrai, A., Spettell, C., Steinberg, G., Mandl, K., Fox, K., Nelson, S. F., et al. (2017) · JAMA Pediatrics

Autism Spectrum Disorder ResearchGenetics and Neurodevelopmental Disorders

Cited by 89

Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland

Blease, C., Kharko, A., Bernstein, M., Bradley, C., Houston, M., Walsh, I., Hägglund, M., DesRoches, C., & Mandl, K. D. (2022) · BMJ Health & Care Informatics

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 81

An i2b2-based, generalizable, open source, self-scaling chronic disease registry

Natter, M. D., Quan, J., Ortiz, D. M., Bousvaros, A., Ilowite, N. T., Inman, C. J., Marsolo, K., McMurry, A. J., Sandborg, C. I., Schanberg, L. E., et al. (2013) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsResearch Data Management Practices

Cited by 73

Fadl Federated-autonomous deep learning for distributed electronic health record

Liu, D., Miller, T., Sayeed, R., & Mandl, K. D. (2018) · arXiv

Privacy-Preserving Technologies in DataMachine Learning in Healthcare

Cited by 65

Author Correction: Developing and adopting safe and effective digital biomarkers to improve patient outcomes

Coravos, A., Khozin, S., & Mandl, K. D. (2019) · npj Digital Medicine

Mobile Health and mHealth ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 34

Discordance between a deep learning model and clinical-grade variant pathogenicity classification in a rare disease cohort

Kong, S. W., Lee, I.-H., Collen, L. V., Field, M., Manrai, A. K., Snapper, S. B., & Mandl, K. D. (2025) · npj Genomic Medicine

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 18

Discordance between a deep learning model and clinical-grade variant pathogenicity classification in a rare disease cohort

Kong, S. W., Lee, I.-H., Collen, L. V., Manrai, A. K., Snapper, S. B., & Mandl, K. D. (2024) · npj Genomic Medicine

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 18