Zhang, J. et al. An interactive dashboard to trace themes, growth maturity, and world fairness in medical synthetic intelligence analysis. Lancet Digital Well being 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. High of Thoughts for High Well being Techniques. https://paddahealth.com/wpcontent/uploads/2020/11/Top_of_Mind_for_Top_Health_Systems_2021_CCM_Reports_FINAL.pdf (2020).
Balakrishnan, T., Chui, M., Corridor, 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. Enterprise Pulse: Funding in AI for healthcare soars. https://dwelling.kpmg/xx/en/dwelling/insights/2018/04/venture-pulse-q1-18-globalanalysis-of-venture-funding.html (2018).
Benjamens, S., Dhunnoo, P. & Meskó, B. The state of synthetic intelligence-based FDA-approved medical gadgets and algorithms: a web based database. npj Digit. Med. 3, 118 (2020).
Wu, E. et al. How medical AI gadgets are evaluated: Limitations and proposals from an evaluation of FDA approvals. Nat. Med. 27, 582–584 (2021).
Lyell, D., Coiera, E., Chen, J., Shah, P. & Magrabi, F. How machine studying is embedded to help clinician determination making: an evaluation of FDA-approved medical gadgets. BMJ Well being Care Inf. 28, e100301 (2021).
McKinney, S. M. et al. Worldwide analysis of an AI system for breast most cancers screening. Nature 577, 89–94 (2020).
Freeman, Ok. et al. Use of synthetic intelligence for picture evaluation in breast most cancers screening programmes: Systematic assessment of take a look at accuracy. BMJ n1872. https://doi.org/10.1136/bmj.n1872 (2021).
Nagendran, M. et al. Synthetic intelligence versus clinicians: systematic assessment of design, reporting requirements, and claims of deep studying research. BMJ m689. https://doi.org/10.1136/bmj.m689 (2020).
Aggarwal, R. et al. Diagnostic accuracy of deep studying in medical imaging: A scientific assessment and meta-analysis. npj Digit. Med. 4, 65 (2021).
Shen, J. et al. Synthetic intelligence versus clinicians in illness analysis: Systematic assessment. 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-Mannequin-centric-to-Knowledge-centric-AI.pdf (2021).
Lowe, D. Machine Studying Deserves Higher Than This. https://www.science.org/content material/blog-post/machine-learning-deserves-better (2021).
Navarro, C. L. A. et al. Danger of bias in research on prediction fashions developed utilizing supervised machine studying strategies: Systematic assessment. BMJ 375, n2281 (2021).
Wilkinson, J. et al. Time to actuality verify the guarantees of machine learning-powered precision drugs. Lancet Digital Well being 2, e677–e680 (2020).
Panch, T., Mattie, H. & Celi, L. A. The “inconvenient fact” about AI in healthcare. npj Digit. Med. 2, 77 (2019).
Wawira Gichoya, J., McCoy, L. G., Celi, L. A. & Ghassemi, M. Fairness in essence: a name for operationalising equity in machine studying for healthcare. BMJ Well being Care Inf. 28, e100289 (2021).
MI in Healthcare Workshop Working Group. Machine intelligence in healthcare—views on trustworthiness, explainability, usability, and transparency. npj Digit. Med. 3, 47 (2020).
Beede, E. et al. A Human-Centered Analysis of a Deep Studying System Deployed in Clinics for the Detection of Diabetic Retinopathy. in Proceedings of the 2020 CHI Convention on Human Elements in Computing Techniques 1–12 (ACM,). https://doi.org/10.1145/3313831.3376718 (2020).
Strickland, E. IBM Watson, heal thyself: How IBM overpromised and underdelivered on AI well being care. IEEE Spectr. 56, 24–31 (2019).
Wong, A. et al. Exterior Validation of a Extensively Carried out Proprietary Sepsis Prediction Mannequin in Hospitalized Sufferers. JAMA Intern. Med. 181, 1065 (2021).
Cam, A. Chui, M. & Corridor, B. World AI Survey: AI proves its price, however few scale influence. (2019).
Rao, A. & Verweij, G. World Synthetic Intelligence Examine: Exploiting the AI Revolution.
Dang, Y., Lin, Q. & Huang, P. AIOps: Actual-World Challenges and Analysis Improvements. In 2019 IEEE/ACM forty first Worldwide Convention on Software program 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 medical influence with synthetic intelligence. BMC Med. 17, 195 (2019).
Reddy, S. et al. Analysis framework to information implementation of AI techniques into healthcare settings. BMJ Well being Care Inf. 28, e100444 (2021).
Gallifant, J. et al. Synthetic intelligence for mechanical air flow: systematic assessment of design, reporting requirements, 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 growth of a reporting guideline (TRIPOD-AI) and threat of bias device (PROBAST-AI) for diagnostic and prognostic prediction mannequin research primarily based on synthetic intelligence. BMJ Open 11, e048008 (2021).
Sounderajah, V. et al. Growing a reporting guideline for synthetic intelligence-centred diagnostic take a look at accuracy research: the STARD-AI protocol. BMJ Open 11, e047709 (2021).
Berisha, V. et al. Digital drugs and the curse of dimensionality. npj Digit. Med. 4, 153 (2021).
Futoma, J., Simons, M., Panch, T., Doshi-Velez, F. & Celi, L. A. The parable of generalisability in medical analysis and machine studying in well being care. Lancet Digital Well being 2, e489–e492 (2020).
Arnold, M. et al. In the direction of Automating the AI Operations Lifecycle. arXiv:2003.12808 [cs] (2020).
Adamson, A. S. & Welch, H. G. Machine studying and the cancer-diagnosis drawback — No gold commonplace. N. Engl. J. Med. 381, 2285–2287 (2019).
Wolff, J., Pauling, J., Keck, A. & Baumbach, J. Systematic Assessment of Financial Impression Research of Synthetic Intelligence in Well being Care. J. Med. Web Res. 22, e16866 (2020).
Zhang, J. et al. Finest practices within the real-world knowledge life cycle. PLOS Digit. Well being 1, e0000003 (2022).
Budrionis, A. & Bellika, J. G. The training healthcare system: The place are we now? A scientific assessment. J. Biomed. Inform. 64, 87–92 (2016).
Whebell, S. & Zhang, J. Bringing organic ARDS phenotypes to the bedside with machine-learning-based classifiers. Lancet Respiratory Med. 10, 319–320 (2022).
Mali, S. A. et al. Making radiomics extra reproducible throughout scanner and imaging protocol variations: A assessment of harmonization. Strategies JPM 11, 842 (2021).
Huser, V., Williams, N. D. & Mayer, C. S. Linking supplier specialty and outpatient diagnoses in medicare claims knowledge: Data high quality implications. Appl Clin. Inf. 12, 729–736 (2021).
Dakka, M. A. et al. Automated detection of poor-quality knowledge: case research in healthcare. Sci. Rep. 11, 18005 (2021).
Sholle, E. T. et al. Underserved populations with lacking race ethnicity knowledge differ considerably from these with structured race/ethnicity documentation. J. Am. Med. Inform. Assoc. 26, 722–729 (2019).
Maille, B. et al. Smartwatch electrocardiogram and synthetic intelligence for assessing cardiac-rhythm security of drug remedy within the COVID-19 pandemic. The QT-logs research. Int. J. Cardiol. 331, 333–339 (2021).
Kwon, J. et al. Synthetic Intelligence-Enhanced Smartwatch ECG for Coronary heart Failure-Lowered Ejection Fraction Detection by Producing 12-Lead ECG. Diagnostics 12, 654 (2022).
Brown, M. & McCool, B. P. Vertical integration: exploration of a preferred strategic idea. Well being 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: Merchandise and Companies. Mayo Clinic Platform https://www.mayoclinicplatform.org/products-and-services/ (2021).
Murugadoss, Ok. et al. Constructing a best-in-class automated de-identification device for digital well being data by means of ensemble studying. Patterns 100255. https://doi.org/10.1016/j.patter.2021.100255 (2021).
Hannah Mitchell. Mayo Clinic AI manufacturing facility has dozens of initiatives underway. Becker’s Hospital Assessment https://www.beckershospitalreview.com/innovation/mayo-clinic-ai-factory-has-dozens-of-projects-underway.html (2021).
Yao, X. et al. Synthetic intelligence–enabled electrocardiograms for identification of sufferers with low ejection fraction: a practical, randomized medical trial. Nat. Med. 27, 815–819 (2021).
Giudicessi, J. R. et al. Synthetic Intelligence–Enabled Evaluation of the Coronary heart Fee Corrected QT Interval Utilizing a Cellular Electrocardiogram System. Circulation 143, 1274–1286 (2021).
Mahayni, A. A. et al. Electrocardiography-Primarily based Synthetic Intelligence Algorithm Aids in Prediction of Lengthy-term Mortality After Cardiac Surgical procedure. Mayo Clin. Proc. 96, 3062–3070 (2021).
McMurry, R. et al. Actual-time evaluation of a mass vaccination effort confirms the protection 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: Rising Capabilities within the Science of Synthetic 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 4 AI startups. Mayo Clinic Information Community https://newsnetwork.mayoclinic.org/dialogue/3-23-mayo-clinic-platform_accelerate-program-begins-with-four-ai-startups/ (2022).
Nakisige, C., Schwartz, M. & Ndira, A. O. Cervical most cancers screening and remedy in Uganda. Gynecologic Oncol. Rep. 20, 37–40 (2017).
William, W., Ware, A., Basaza-Ejiri, A. H. & Obungoloch, J. A assessment of picture evaluation and machine studying strategies for automated cervical most cancers screening from pap-smear photos. Pc Strategies Prog. Biomedicine 164, 15–22 (2018).
William, W., Ware, A., Basaza-Ejiri, A. H. & Obungoloch, J. A pap-smear evaluation device (PAT) for detection of cervical most cancers from pap-smear photos. BioMed. Eng. OnLine 18, 16 (2019).
AIX-COVNET et al. Widespread pitfalls and proposals for utilizing machine studying to detect and prognosticate for COVID-19 utilizing chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021).
Wynants, L. et al. Prediction fashions for analysis and prognosis of covid-19: systematic assessment and important appraisal. BMJ m1328. https://doi.org/10.1136/bmj.m1328 (2020).
Dan Bamford & Samantha Gan. NHS X – AI in Well being and Care Award. (2020).
Richard Torbett. Fashions of Entry to Well being Knowledge within the UK. (2022).
Rieke, N. et al. The way forward for digital well being with federated studying. npj Digit. Med. 3, 119 (2020).
AI Centre for Worth Primarily based Healthcare. AI4VBH: Platforms. https://www.aicentre.co.uk/platforms#view2 (2022).
US FDA Heart for Units and Radiological Well being. Synthetic Intelligence/Machine Studying (AI/ML)-Primarily based Software program as a Medical System (SaMD) Motion Plan. (2021).
Medicines & Healthcare Merchandise Regulatory Company. Software program and AI as a Medical System Change Programme. (2021).
John, M. M., Olsson, H. H. & Bosch, J. In the direction of MLOps: A Framework and Maturity Mannequin. in 2021 forty seventh Euromicro Convention on Software program Engineering and Superior Purposes (SEAA) 1–8 (IEEE, 2021). https://doi.org/10.1109/SEAA53835.2021.00050.
