Zhang, J. et al. An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research. Lancet Digital Health 4, e212–e213 (2022).
Pretnik, R. & Krotz, L. Healthcare AI 2020. https://klasresearch.com/report/healthcare-ai-2020-investment-continuesbut-results-slower-than-expected-a-decision-insights-report/1443 (2020).
Rob, B. et al. Top of Mind for Top Health Systems. https://paddahealth.com/wpcontent/uploads/2020/11/Top_of_Mind_for_Top_Health_Systems_2021_CCM_Reports_FINAL.pdf (2020).
Balakrishnan, T., Chui, M., Hall, B. & Henke, N. The State of AI in 2020. https://www.mckinsey.com/business-functions/quantumblack/ourinsights/global-survey-the-state-of-ai-in-2020 (2020).
Lavender, J. Venture Pulse: Investment in AI for healthcare soars. https://home.kpmg/xx/en/home/insights/2018/04/venture-pulse-q1-18-globalanalysis-of-venture-funding.html (2018).
Benjamens, S., Dhunnoo, P. & Meskó, B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. npj Digit. Med. 3, 118 (2020).
Wu, E. et al. How medical AI devices are evaluated: Limitations and recommendations from an analysis of FDA approvals. Nat. Med. 27, 582–584 (2021).
Lyell, D., Coiera, E., Chen, J., Shah, P. & Magrabi, F. How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices. BMJ Health Care Inf. 28, e100301 (2021).
McKinney, S. M. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020).
Freeman, K. et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: Systematic review of test accuracy. BMJ n1872. https://doi.org/10.1136/bmj.n1872 (2021).
Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ m689. https://doi.org/10.1136/bmj.m689 (2020).
Aggarwal, R. et al. Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis. npj Digit. Med. 4, 65 (2021).
Shen, J. et al. Artificial intelligence versus clinicians in disease diagnosis: Systematic review. JMIR Med. Inf. 7, e10010 (2019).
Andrew Ng. MLOps: From model-centric to data-centric AI. https://www.deeplearning.ai/wpcontent/uploads/2021/06/MLOps-From-Model-centric-to-Data-centric-AI.pdf (2021).
Lowe, D. Machine Learning Deserves Better Than This. https://www.science.org/content/blog-post/machine-learning-deserves-better (2021).
Navarro, C. L. A. et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: Systematic review. BMJ 375, n2281 (2021).
Wilkinson, J. et al. Time to reality check the promises of machine learning-powered precision medicine. Lancet Digital Health 2, e677–e680 (2020).
Panch, T., Mattie, H. & Celi, L. A. The “inconvenient truth” about AI in healthcare. npj Digit. Med. 2, 77 (2019).
Wawira Gichoya, J., McCoy, L. G., Celi, L. A. & Ghassemi, M. Equity in essence: a call for operationalising fairness in machine learning for healthcare. BMJ Health Care Inf. 28, e100289 (2021).
MI in Healthcare Workshop Working Group. Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency. npj Digit. Med. 3, 47 (2020).
Beede, E. et al. A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–12 (ACM,). https://doi.org/10.1145/3313831.3376718 (2020).
Strickland, E. IBM Watson, heal thyself: How IBM overpromised and underdelivered on AI health care. IEEE Spectr. 56, 24–31 (2019).
Wong, A. et al. External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. JAMA Intern. Med. 181, 1065 (2021).
Cam, A. Chui, M. & Hall, B. Global AI Survey: AI proves its worth, but few scale impact. (2019).
Rao, A. & Verweij, G. Global Artificial Intelligence Study: Exploiting the AI Revolution.
Dang, Y., Lin, Q. & Huang, P. AIOps: Real-World Challenges and Research Innovations. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) 4–5 (IEEE, 2019). https://doi.org/10.1109/ICSE-Companion.2019.00023.
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019).
Reddy, S. et al. Evaluation framework to guide implementation of AI systems into healthcare settings. BMJ Health Care Inf. 28, e100444 (2021).
Gallifant, J. et al. Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias. British Journal of Anaesthesia S0007091221006206, https://doi.org/10.1016/j.bja.2021.09.025 (2021).
Collins, G. S. et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11, e048008 (2021).
Sounderajah, V. et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 11, e047709 (2021).
Berisha, V. et al. Digital medicine and the curse of dimensionality. npj Digit. Med. 4, 153 (2021).
Futoma, J., Simons, M., Panch, T., Doshi-Velez, F. & Celi, L. A. The myth of generalisability in clinical research and machine learning in health care. Lancet Digital Health 2, e489–e492 (2020).
Arnold, M. et al. Towards Automating the AI Operations Lifecycle. arXiv:2003.12808 [cs] (2020).
Adamson, A. S. & Welch, H. G. Machine learning and the cancer-diagnosis problem — No gold standard. N. Engl. J. Med. 381, 2285–2287 (2019).
Wolff, J., Pauling, J., Keck, A. & Baumbach, J. Systematic Review of Economic Impact Studies of Artificial Intelligence in Health Care. J. Med. Internet Res. 22, e16866 (2020).
Zhang, J. et al. Best practices in the real-world data life cycle. PLOS Digit. Health 1, e0000003 (2022).
Budrionis, A. & Bellika, J. G. The learning healthcare system: Where are we now? A systematic review. J. Biomed. Inform. 64, 87–92 (2016).
Whebell, S. & Zhang, J. Bringing biological ARDS phenotypes to the bedside with machine-learning-based classifiers. Lancet Respiratory Med. 10, 319–320 (2022).
Mali, S. A. et al. Making radiomics more reproducible across scanner and imaging protocol variations: A review of harmonization. Methods JPM 11, 842 (2021).
Huser, V., Williams, N. D. & Mayer, C. S. Linking provider specialty and outpatient diagnoses in medicare claims data: Data quality implications. Appl Clin. Inf. 12, 729–736 (2021).
Dakka, M. A. et al. Automated detection of poor-quality data: case studies in healthcare. Sci. Rep. 11, 18005 (2021).
Sholle, E. T. et al. Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation. J. Am. Med. Inform. Assoc. 26, 722–729 (2019).
Maille, B. et al. Smartwatch electrocardiogram and artificial intelligence for assessing cardiac-rhythm safety of drug therapy in the COVID-19 pandemic. The QT-logs study. Int. J. Cardiol. 331, 333–339 (2021).
Kwon, J. et al. Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG. Diagnostics 12, 654 (2022).
Brown, M. & McCool, B. P. Vertical integration: exploration of a popular strategic concept. Health Care Manag. Rev. 11, 7–19 (1986).
Kumpe, T. & Bolwijn, P. T. Manufacturing: The New Case for Vertical Integration. 8, 75 (1988).
Mayo Clinic Platform. Mayo Clinic Platform: Products and Services. Mayo Clinic Platform https://www.mayoclinicplatform.org/products-and-services/ (2021).
Murugadoss, K. et al. Building a best-in-class automated de-identification tool for electronic health records through ensemble learning. Patterns 100255. https://doi.org/10.1016/j.patter.2021.100255 (2021).
Hannah Mitchell. Mayo Clinic AI factory has dozens of projects underway. Becker’s Hospital Review https://www.beckershospitalreview.com/innovation/mayo-clinic-ai-factory-has-dozens-of-projects-underway.html (2021).
Yao, X. et al. Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat. Med. 27, 815–819 (2021).
Giudicessi, J. R. et al. Artificial Intelligence–Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device. Circulation 143, 1274–1286 (2021).
Mahayni, A. A. et al. Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery. Mayo Clin. Proc. 96, 3062–3070 (2021).
McMurry, R. et al. Real-time analysis of a mass vaccination effort confirms the safety of FDA-authorized mRNA vaccines for COVID-19 from Moderna and Pfizer/BioNtech. http://medrxiv.org/lookup/doi/10.1101/2021.02.20.21252134 (2021).
Mayo Clinic. Mayo Clinic: Emerging Capabilities in the Science of Artificial Intelligence. Mayoclinic.org https://www.mayoclinic.org/giving-to-mayo-clinic/our-priorities/artificial-intelligence (2021).
Susan Barber Lindquist. Mayo Clinic Platform_Accelerate program begins with four AI startups. Mayo Clinic News Network https://newsnetwork.mayoclinic.org/discussion/3-23-mayo-clinic-platform_accelerate-program-begins-with-four-ai-startups/ (2022).
Nakisige, C., Schwartz, M. & Ndira, A. O. Cervical cancer screening and treatment in Uganda. Gynecologic Oncol. Rep. 20, 37–40 (2017).
William, W., Ware, A., Basaza-Ejiri, A. H. & Obungoloch, J. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images. Computer Methods Prog. Biomedicine 164, 15–22 (2018).
William, W., Ware, A., Basaza-Ejiri, A. H. & Obungoloch, J. A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images. BioMed. Eng. OnLine 18, 16 (2019).
AIX-COVNET et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021).
Wynants, L. et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ m1328. https://doi.org/10.1136/bmj.m1328 (2020).
Dan Bamford & Samantha Gan. NHS X – AI in Health and Care Award. (2020).
Richard Torbett. Models of Access to Health Data in the UK. (2022).
Rieke, N. et al. The future of digital health with federated learning. npj Digit. Med. 3, 119 (2020).
AI Centre for Value Based Healthcare. AI4VBH: Platforms. https://www.aicentre.co.uk/platforms#view2 (2022).
US FDA Center for Devices and Radiological Health. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. (2021).
Medicines & Healthcare Products Regulatory Agency. Software and AI as a Medical Device Change Programme. (2021).
John, M. M., Olsson, H. H. & Bosch, J. Towards MLOps: A Framework and Maturity Model. in 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 1–8 (IEEE, 2021). https://doi.org/10.1109/SEAA53835.2021.00050.
