Dr Joseph E. Alderman MBChB FRCA PhD

Dr Joseph Alderman

Department of Inflammation and Ageing
NIHR Clinical Lecturer in Anaesthetics
Postdoctoral Researcher in AI and Digital Health

Contact details

Email
j.e.alderman@bham.ac.uk
Twitter
@jaldmn
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Address
Department of Inflammation and Ageing
College of Medicine and Health
University of Birmingham
Edgbaston
Birmingham
B15 2TT
Founders' Awards 2024 - The Standing Together team | University of Birmingham

Dr Joseph Alderman is a practicing NHS anaesthetist, and a postdoctoral researcher with expertise in the evaluation and regulation of artificial intelligence, clinical prediction models, and other data-driven health technologies.

His research and contributions to health policy are focused on ensuring that all patients can benefit from these transformative innovations.

He is co-lead for the STANDING Together initiative, which has brought together more than 350 collaborators from 58 countries to build recommendations to improve the way healthcare datasets are used when creating healthcare AI technologies. He also co-organises the clinical AI interest group at The Alan Turing Institute, and is co-opted to the Professional Affairs and Safety committee at the Faculty of Intensive Care Medicine.

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Qualifications

  • PhD in the evaluation and regulation of clinical prediction models and artificial intelligence, University of Birmingham, 2025
  • FRCA - Fellowship of the Royal College of Anaesthetists, 2022
  • PGCert in Clinical Research Methodology (with distinction), University of Birmingham, 2022
  • MBChB, University of Birmingham, 2015
  • BMedSc (Hons), University of Birmingham, 2014

Biography

Dr Alderman is a clinical academic whose research focuses on the governance and oversight of artificial intelligence and other data-driven health technologies, ensuring that these tools are implemented safely, effectively, and equitably.

He completed his PhD at the University of Birmingham, entitled 'Protecting Patients from Problematic Predictions'. Using a mixed-methods approach, his research explored how clinical predictive models are used and governed within perioperative care. This work identified important gaps in the current oversight structures, and generated a series of recommendations to improve the evidence base, transparency, and post-deployment monitoring of these tools.

During his doctoral research, he also co-led the STANDING Together initiative, an international collaboration that developed consensus recommendations to tackle algorithmic bias and promote transparency in health datasets. Involving over 350 representatives from 58 countries, the project produced guidance for dataset curators and AI developers to help identify and mitigate biases that could exacerbate health inequalities. The final recommendations were published simultaneously in The Lancet Digital Health and NEJM AI.

Dr Alderman’s clinical background is in anaesthesia and intensive care medicine. He is a Specialty Registrar in the West Midlands and a Fellow of the Royal College of Anaesthetists (FRCA). He graduated from the University of Birmingham (MBChB, 2015) and previously held an NIHR Academic Clinical Fellowship, during which he led an NIAA-funded laboratory study into the immunometabolic effects of hyperlactataemia in critical illness.

His PhD was supervised by Dr Xiaoxuan Liu, Professor Alastair Denniston, Dr Dhruv Parekh, and Professor Richard Riley from the University of Birmingham, and by Professor Charlotte Summers from the University of Cambridge.

Teaching

Postgraduate supervision

Dr Alderman supervises doctoral students from both clinical and non-clinical backgrounds who wish to study for a PhD or MD. If you are interested in any of the subject areas described in the bio above and would like to explore options, please contact him via email: j.e.alderman@bham.ac.uk.

Research

Dr Alderman’s main research interest is in the intersection between artificial intelligence and healthcare. His key focuses are:

1. How do we ensure patient safety when AI is used in healthcare?

2. How can we promote digital health equity, particularly considering the evidence that AI can contribute to bias and worsen health outcomes for those already experiencing disadvantage in society?

3. How can we ‘prove’ that AI in healthcare works as expected?

4. How can we meaningfully involve patients, carers, members of the public, those with lived experience of health and social care, clinicians and other stakeholder groups when designing, implementing and monitoring AI health technologies?

5. How can we ensure good governance of data-driven health technologies which are provided as a public good, rather than as a commercial product?

Publications

Recent publications

Article

Riley, R, Collins, G, Archer, L, Whittle, R, Legha, A, Kirton, L, Dhiman, P, Sadatsafavi, M, Adderley, N, Alderman, J, Martin, GP & Ensor, J 2025, 'A decomposition of Fisher’s information to inform sample size for developing or updating fair and precise clinical prediction models - Part 2: time-to-event outcomes', Diagnostic and Prognostic Research.

Davenport, C, Richter, A, Hillier, B, Scandrett, K, Agarwal, R, Baldwin, SW, Kale, AU, Alderman, J, Macdonald, T & Deeks, JJ 2025, 'Direct-to-consumer self-tests sold in the UK in 2023: cross sectional review of information on intended use, instructions for use, and post-test decision making', BMJ, vol. 390, e085546. https://doi.org/10.1136/bmj-2025-085546

Hillier, B, Deeks, JJ, Alderman, J, Kale, AU, Macdonald, T, Baldwin, SW, Scandrett, K, Agarwal, R, Richter, A & Davenport, C 2025, 'Direct-to-consumer self-tests sold in the UK in 2023: cross sectional review of regulation and evidence of performance', BMJ, vol. 390 , e085547. https://doi.org/10.1136/bmj-2025-085547

Whittle, R, Ensor, J, Archer, L, Collins, GS, Dhiman, P, Denniston, A, Alderman, J, Legha, A, van Smeden, M, Moons, KG, Cazier, J-B, Riley, RD & Snell, KIE 2025, 'Extended sample size calculations for evaluation of prediction models using a threshold for classification', BMC Medical Research Methodology, vol. 25, no. 1, 170. https://doi.org/10.1186/s12874-025-02592-4

Alderman, J, Riley, R, Parekh, D, Summers, C, Liu, X & Denniston, A 2025, 'Hidden risks of predictive models in healthcare', BMJ evidence-based medicine. https://doi.org/10.1136/bmjebm-2025-113730

Riley, RD, Ensor, J, Snell, KIE, Archer, L, Whittle, R, Dhiman, P, Alderman, J, Liu, X, Kirton, L, Manson-Whitton, J, van Smeden, M, Nirantharakumar, K, Denniston, AK, Van Calster, B & Collins, G 2025, 'Importance of sample size on the quality and utility of AI-based prediction models for healthcare', The Lancet Digital Health. https://doi.org/10.1016/j.landig.2025.01.013

Riley, RD, Collins, G, Kirton, L, Snell, KIE, Ensor, J, Whittle, R, Dhiman, P, van Smeden, M, Liu, X, Alderman, J, Nirantharakumar, K, Manson-Whitton, J, Westwood, AJ, Cazier, J-B, Moons, KGM, Martin, GP, Sperrin, M, Denniston, AK, Jr, FEH & Archer, L 2025, 'Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches', BMJ, vol. 388, e080749. https://doi.org/10.1136/bmj-2024-080749

Chotalia, M, Ali, M, Alderman, JE, Bansal, S, Patel, JM, Bangash, MN & Parekh, D 2023, 'Cardiovascular Subphenotypes in Acute Respiratory Distress Syndrome', Critical care medicine, vol. 51, no. 4, pp. 460-470. https://doi.org/10.1097/ccm.0000000000005751

Arora, A, Alderman, JE, Palmer, J, Ganapathi, S, Laws, E, McCradden, MD, Oakden-Rayner, L, Pfohl, SR, Ghassemi, M, Mckay, F, Treanor, D, Rostamzadeh, N, Mateen, BA, Gath, J, Adebajo, AO, Kuku, S, Matin, RN, Heller, K, Sapey, E, Sebire, NJ, Cole-Lewis, H, Calvert, M, Denniston, A & Liu, X 2023, 'The value of standards for health datasets in artificial intelligence-based applications', Nature Medicine, vol. 29, no. 11, pp. 2929-2938. https://doi.org/10.1038/s41591-023-02608-w

Editorial

Liu, X, Alderman, J & Laws, E 2024, 'A Global Health Data Divide', NEJM AI, vol. 1, no. 6. https://doi.org/10.1056/AIe2400388

Preprint

Riley, RD, Collins, GS, Archer, L, Whittle, R, Legha, A, Kirton, L, Dhiman, P, Sadatsafavi, M, Adderley, NJ, Alderman, J, Martin, GP & Ensor, J 2025 'A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- Part 2: time-to-event outcomes' arXiv. <https://arxiv.org/abs/2501.14482>

Whittle, R, Ensor, J, Archer, L, Collins, GS, Dhiman, P, Denniston, A, Alderman, J, Legha, A, van Smeden, M, Moons, KG, Cazier, J-B, Riley, RD & Snell, KIE 2024 'Extended sample size calculations for evaluation of prediction models using a threshold for classification' arXiv. https://doi.org/10.48550/arXiv.2406.19673

Review article

Laws, E, Charalambides, M, Vadera, S, Keller, E, Alderman, J, Blackboro, B, Hogg, J, Salisbury, T, Palmer, J, Calvert, M, Mackintosh, M, Matin, R, Sapey, E, Ordish, J, McCradden, M, Mateen, B, Gath, J, Adebajo, A, Kuku, S, Bradlow, W, Denniston, AK & Liu, X 2025, 'Diversity and inclusion within datasets in heart failure: A systematic review ', JACC: Advances, vol. 4, no. 3, 101610. https://doi.org/10.1016/j.jacadv.2025.101610

Alderman, JE, Palmer, J, Laws, E, McCradden, MD, Ordish, J, Ghassemi, M, Pfohl, SR, Rostamzadeh, N, Cole-Lewis, H, Glocker, B, Calvert, M, Pollard, TJ, Gill, J, Gath, J, Adebajo, A, Beng, J, Leung, CH, Kuku, S, Farmer, L-A, Matin, RN, Mateen, BA, McKay, F, Heller, K, Karthikesalingam, A, Treanor, D, Mackintosh, M, Oakden-Rayner, L, Pearson, R, Manrai, AK, Myles, P, Kumuthini, J, Kapacee, Z, Sebire, NJ, Nazer, LH, Seah, J, Akbari, A, Berman, L, Gichoya, JW, Righetto, L, Samuel, D, Wasswa, W, Charalambides, M, Arora, A, Pujari, S, Summers, C, Sapey, E, Wilkinson, S, Thakker, V, Denniston, A & Liu, X 2025, 'Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations', The Lancet Digital Health, vol. 7, no. 1, pp. e64-e88. https://doi.org/10.1016/S2589-7500(24)00224-3

Alderman, J, Charalambides, M, Sachdeva, G, Laws, E, Palmer, J, Lee, E, Menon, V, Malik, Q, Vadera, S, Calvert, M, Ghassemi, M, McCradden, MD, Ordish, J, Mateen, B, Summers, C, Gath, J, Matin, RN, Denniston, AK & Liu, X 2024, 'Revealing transparency gaps in publicly available Covid-19 datasets used for medical artificial intelligence development: a systematic review', The Lancet Digital Health, vol. 6, no. 11, pp. e827-e847. https://doi.org/10.1016/S2589-7500(24)00146-8

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