Perioperative prediction - FICM hot topics

FICM hot topics - resources and contact

Hi there! 👋

I'm Joe - an NIHR Clinical Lecturer working at the University of Birmingham, UK.

My clinical specialities are anaesthesia and intensive care medicine.
My PhD focused on the governance, oversight and assurance of AI, predictive models, and other data-driven health technologies.

I'm part of a team focused on the evaluation and regulation of AI and digital health technologies 📈🏥

We ask 3 main questions:

Are they safe?
Are they effective?
Are they equitable?

To learn more about what we do, visit https://www.cersi-ai.org/

FICM position statement on medical AI

The Faculty of Intensive Care Medicine has published a position statement on medical AI. To read it, visit https://www.ficm.ac.uk/standardssafetyguidelinesclinicalquality/ficm-position-statement-on-medical-ai

STANDING Together

The final STANDING Together recommendations have now been published in The Lancet Digital Health and NEJM-AI. To learn more or to read the full paper, visit www.datadiversity.org/recommendations

Dr Joseph Alderman
BMedSc (Hons) MBChB FRCA PhD

Click the links above to connect via LinkedIn or Twitter/X, or to drop me an email.

References for my presentation at FICM hot topics:

U.S. Food and Drug Administration. ‘Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices’. FDA, 2023. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices.
Liu, Xiaoxuan, Livia Faes, Aditya U. Kale, Siegfried K. Wagner, Dun Jack Fu, Alice Bruynseels, Thushika Mahendiran, et al. ‘A Comparison of Deep Learning Performance against Health-Care Professionals in Detecting Diseases from Medical Imaging: A Systematic Review and Meta-Analysis’. The Lancet Digital Health 1, no. 6 (1 October 2019): e271–97. https://doi.org/10.1016/S2589-7500(19)30123-2.
Saab, Khaled, Tao Tu, Wei-Hung Weng, Ryutaro Tanno, David Stutz, Ellery Wulczyn, Fan Zhang, et al. ‘Capabilities of Gemini Models in Medicine’. arXiv, 1 May 2024. https://doi.org/10.48550/arXiv.2404.18416.
Ng, Annie Y., Cary J. G. Oberije, Éva Ambrózay, Endre Szabó, Orsolya Serfőző, Edit Karpati, Georgia Fox, et al. ‘Prospective Implementation of AI-Assisted Screen Reading to Improve Early Detection of Breast Cancer’. Nature Medicine 29, no. 12 (December 2023): 3044–49. https://doi.org/10.1038/s41591-023-02625-9.
Wong, Andrew, Erkin Otles, John P. Donnelly, Andrew Krumm, Jeffrey McCullough, Olivia DeTroyer-Cooley, Justin Pestrue, et al. ‘External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients’. JAMA Internal Medicine 181, no. 8 (1 August 2021): 1065–70. https://doi.org/10.1001/jamainternmed.2021.2626.
Cao, Jie, Xiaosong Zhang, Vahakn Shahinian, Huiying Yin, Diane Steffick, Rajiv Saran, Susan Crowley, et al. ‘Generalizability of an Acute Kidney Injury Prediction Model across Health Systems’. Nature Machine Intelligence 4, no. 12 (December 2022): 1121–29. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10751025/
Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. ‘Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations’. Science 366, no. 6464 (25 October 2019): 447–53. https://doi.org/10.1126/science.aax2342.
Seyyed-Kalantari, Laleh, Haoran Zhang, Matthew B. A. McDermott, Irene Y. Chen, and Marzyeh Ghassemi. ‘Underdiagnosis Bias of Artificial Intelligence Algorithms Applied to Chest Radiographs in Under-Served Patient Populations’. Nature Medicine 27, no. 12 (December 2021): 2176–82. https://doi.org/10.1038/s41591-021-01595-0.
Chen, Irene Y., Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi. ‘Ethical Machine Learning in Healthcare’. Annual Review of Biomedical Data Science 4 (July 2021): 123–44. https://doi.org/10.1146/annurev-biodatasci-092820-114757.
‘AI Act | Shaping Europe’s Digital Future’, 30 July 2024. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai.
GOV.UK. ‘AI Airlock: The Regulatory Sandbox for AIaMD’, 9 May 2024. https://www.gov.uk/government/collections/ai-airlock-the-regulatory-sandbox-for-aiamd.
U.S. Food and Drug Administration, Health Canada, and Medicines and Healthcare products Regulatory Agency. ‘Good Machine Learning Practice for Medical Device Development: Guiding Principles’. FDA, 27 October 2021. https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles.
Liu, Xiaoxuan, Ben Glocker, Melissa M. McCradden, Marzyeh Ghassemi, Alastair K. Denniston, and Lauren Oakden-Rayner. ‘The Medical Algorithmic Audit’. The Lancet Digital Health (5 April 2022). https://doi.org/10.1016/S2589-7500(22)00003-6.
Cruz Rivera, Samantha, Xiaoxuan Liu, An-Wen Chan, Alastair K. Denniston, and Melanie J. Calvert. ‘Guidelines for Clinical Trial Protocols for Interventions Involving Artificial Intelligence: The SPIRIT-AI Extension’. Nature Medicine 26, no. 9 (September 2020): 1351–63. https://doi.org/10.1038/s41591-020-1037-7.
Vasey, Baptiste, Myura Nagendran, Bruce Campbell, David A Clifton, Gary S Collins, Spiros Denaxas, Alastair K Denniston, et al. ‘Reporting Guideline for the Early Stage Clinical Evaluation of Decision Support Systems Driven by Artificial Intelligence: DECIDE-AI’. BMJ, 18 May 2022, e070904. https://doi.org/10.1136/bmj-2022-070904.
Collins, Gary S., Karel G. M. Moons, Paula Dhiman, Richard D. Riley, Andrew L. Beam, Ben Van Calster, Marzyeh Ghassemi, et al. ‘TRIPOD+AI Statement: Updated Guidance for Reporting Clinical Prediction Models That Use Regression or Machine Learning Methods’. BMJ 385 (16 April 2024): e078378. https://doi.org/10.1136/bmj-2023-078378.
Liu, Xiaoxuan, Samantha Cruz Rivera, David Moher, Melanie J. Calvert, and Alastair K. Denniston. ‘Reporting Guidelines for Clinical Trial Reports for Interventions Involving Artificial Intelligence: The CONSORT-AI Extension’. Nature Medicine 26, no. 9 (September 2020): 1364–74. https://doi.org/10.1038/s41591-020-1034-x.
‘JMIR Research Protocols - Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study’. Accessed 12 August 2024. https://www.researchprotocols.org/2024/1/e50568.
Alderman JE, Palmer J, Laws E, McCradden MD, Ordish J, Ghassemi M, Pfohl SR, et al. ‘Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations’. NEJM AI 2, no. 1 (January 2025): AIp2401088. https://doi.org/10.1056/AIp2401088.
Alderman JE, Palmer J, Laws E, McCradden MD, Ordish J, Ghassemi M, Pfohl SR, et al. ‘Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations’. The Lancet Digital Health 7, no. 1 (1 January 2025): e64–88. https://doi.org/10.1016/S2589-7500(24)00224-3.
‘Economic Evaluation for Medical Artificial Intelligence: Accuracy vs. Cost-Effectiveness in a Diabetic Retinopathy Screening Case | Npj Digital Medicine’. Accessed 12 August 2024. https://www.nature.com/articles/s41746-024-01032-9.
‘The Environmental Cost of AI’. Accessed 12 August 2024. https://www.ft.com/content/323299dc-d9d5-482e-9442-f4516f6753f0.

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