Evidence map›Paper›PMID 40463916›Full record

SynthesisFrontiers in health services2025

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

Trystan B Macdonald, H D Jeffry Hogg, Jacqueline Dinnes, Lucy Verrinder, Gregory Maniatopoulos, Sian Taylor-Phillips, Bethany Shinkins, J Kevin Dunbar, Ameenat Lola Solebo, Hannah Sutton and 9 more

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in health services, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Trystan B MacdonaldUniversity Hospitals Birmingham NHSFT, Birmingham, United Kingdom.
H D Jeffry HoggUniversity Hospitals Birmingham NHSFT, Birmingham, United Kingdom.
Jacqueline DinnesNIHR Birmingham Biomedical Research Centre, Birmingham, United Kingdom.
Lucy VerrinderLion Health, Stourbridge, United Kingdom.
Gregory ManiatopoulosThe University of Leicester, Leicester, United Kingdom.
Sian Taylor-PhillipsWarwick Screening, Warwick Medical School, University of Warwick, Coventry, United Kingdom.
Bethany ShinkinsWarwick Applied Health, University of Warwick, Coventry, United Kingdom.
J Kevin DunbarVaccination and Screening Directorate, NHS England, London, United Kingdom.
Ameenat Lola SoleboMoorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.
Hannah SuttonIndependent Researcher, Oxford, United Kingdom.
John AttwoodAlder Hey Children's Hospital, Liverpool, United Kingdom.
Michael PogoseHardian Health, London, United Kingdom.
Rosalind Given-WilsonSt. George's University Hospitals NHSFT, London, United Kingdom.
Felix GreavesDigital Policy Unit, Department of Health and Social Care, London, United Kingdom.
Carl MacraeNottingham University Business School, University of Nottingham, Nottingham, United Kingdom.
Russell PearsonMedicines and Healthcare Products Regulatory Agency, London, United Kingdom.
Adnan TufailMoorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.
Xiaoxuan LiuNIHR Birmingham Biomedical Research Centre, Birmingham, United Kingdom.
Alastair K DennistonUniversity Hospitals Birmingham NHSFT, Birmingham, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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. A total of 14 TPPs were identified, most targeted at low- and middle-income settings and communicable diseases. Only one TPP outlined the requirements for an AI device specifically. In total, 248 different characteristics were reported across the TPPs identified and 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.

Indexed as

AIdigital health technologyquality by designtarget product profileTPP

Identifiers

PMID40463916
PMCPMC12131871

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.