Target product profiles for digital health technologies including those with artificial intelligence: a systematic review

Macdonald, Trystan  Barclay; Hogg, Henry  David Jeffry; Dinnes, Jacqueline; Verrinder, Lucy  May; Maniatopoulos, Gregory; Taylor-Phillips, Sian; Shinkins, Bethany; Dunbar, Kevin; Solebo, Ameenat Lola; Sutton, Hannah; Attwood, John; Pogose, Michael; Given-Wilson, Rosalind; Greaves, Felix; Macrae, Carl; Pearson, Russell; Tufail, Adnan; Liu, Xiaoxuan; Denniston, Alastair

doi:10.3389/frhs.2025.1537016

SYSTEMATIC REVIEW article

Front. Health Serv.

Sec. Implementation Science

Volume 5 - 2025 | doi: 10.3389/frhs.2025.1537016

This article is part of the Research TopicPlace-based Evidence for Clinical Artificial Intelligence ImplementationView all articles

Target product profiles for digital health technologies including those with artificial intelligence: a systematic review

Provisionally accepted

Trystan Barclay Macdonald^1,2,3*

Henry David Jeffry Hogg^1,4

Jacqueline Dinnes^5,6

Lucy May Verrinder⁷

Gregory Maniatopoulos⁸

Sian Taylor-Phillips⁹

Bethany Shinkins¹⁰

Kevin Dunbar¹¹

Ameenat Lola Solebo^12,13

Hannah Sutton¹⁴

John Attwood¹⁵

Michael Pogose¹⁶

Rosalind Given-Wilson¹⁷

Felix Greaves^18,19

Carl Macrae²⁰

Russell Pearson²¹

Adnan Tufail^13,22

Xiaoxuan Liu^23,24,6

Alastair Denniston^1,2,24,25,3

¹University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
²Academic Unit of Ophthalmology, Institute of Inflammation and Aging, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom, Birmingham, United Kingdom
³NIHR Birmingham Biomedical Research Centre, Birmingham, United Kingdom
⁴Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
⁵Biostatistics, Evidence Synthesis and Test Evaluation and Modelling, Institute of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom
⁶National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, United Kingdom
⁷Lion Health, Stourbridge, United Kingdom
⁸The University of Leicester, Leicester, United Kingdom
⁹Warwick Screening, Warwick Medical School, University of Warwick, Coventry, United Kingdom
¹⁰Warwick Applied Health, University of Warwick, Coventry, United Kingdom
¹¹Vaccination and Screening Directorate, NHS England, London, United Kingdom
¹²Population Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
¹³Moorfields Eye Hospital, NHS Foundation Trust, London, United Kingdom
¹⁴Lay Member, Oxford, England, United Kingdom
¹⁵Alder Hey Children's Hospital, Liverpool, United Kingdom
¹⁶Hardian Health, London, United Kingdom
¹⁷St George’s University Hospitals NHS Foundation Trust, London, England, United Kingdom
¹⁸Digital Policy Unit, Department of Health and Social Care, London, United Kingdom
¹⁹School of Public Health, Faculty of Medicine, Imperial College London, London, England, United Kingdom
²⁰Nottingham University Business School, University of Nottingham, Nottingham, United Kingdom
²¹Medicines and Healthcare products Regulatory Agency (United Kingdom), London, United Kingdom
²²Institute of Ophthalmology, Faculty of Brain Sciences, University College London, London, England, United Kingdom
²³College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
²⁴Centre for Regulatory Science and Innovation, Birmingham Health Partners, Birmingham, United Kingdom
²⁵NIHR Biomedical Research Centre at Moorfields and UCL Institute of Ophthalmology, London, United Kingdom

The final, formatted version of the article will be published soon.

Digital health technologies (DHTs), including those incorporating artificial intelligence (AI), have the potential to improve healthcare access, efficiency, and quality, reducing gaps between healthcare capacity and demand. Despite prioritisation in health policy, the adoption of DHTs remains limited, especially for AI, in part due to complex system requirements. Target Product Profiles (TPPs) are documents outlining the characteristics necessary for medical technologies to be utilised in practice, and offer a way to align DHTs' research and development with health systems' needs. This systematic review examines current DHT TPPs' methodologies, stakeholders, and contents. 14 TPPs were identified, most targeted at low-and middle-income settings and communicable diseases. Only one outlined the requirements for an AI device specifically. 248 different characteristics were reported across the TPPs identified, these were consolidated down to 33 key characteristics. Some considerations for DHTs' successful adoption, such as regulatory requirements or environmental sustainability, were reported inconsistently or not at all. There was little standardisation in TPP development or contents, and limited transparency in reporting. Our findings emphasise the need for guidelines for TPP development, could help inform these, and could be used as a basis to develop future DHT TPPs.

Keywords: target product profile, TPP, Quality by design, Digital health technology, AI

Received: 29 Nov 2024; Accepted: 14 Mar 2025.

Copyright: © 2025 Macdonald, Hogg, Dinnes, Verrinder, Maniatopoulos, Taylor-Phillips, Shinkins, Dunbar, Solebo, Sutton, Attwood, Pogose, Given-Wilson, Greaves, Macrae, Pearson, Tufail, Liu and Denniston. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Trystan Barclay Macdonald, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom

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