Mapping AI startup investment and innovation in healthcare using a five-tier AI systems complexity framework
File(s)
Published version
Author(s)
Zahlan, Ahmed
Soh, Pek Hooi
Clarysse, Bart
Type
Journal Article
Abstract
Artificial Intelligence (AI) is reshaping healthcare through advances in diagnostics, treatment, and operations, yet the startup ecosystem driving this transformation remains underexplored. Analyzing 3,807 AI health startups founded between 2010 and 2024, this study applies a five-tier framework of AI systems complexity to classify ventures by medical domain, AI systems level, funding, geography, and team composition. Nearly two-thirds of AI investments focus on clinical decision support, drug discovery, and diagnostics, domains associated with higher-complexity deep-learning systems, while areas such as mental health, public health, and rehabilitation attract less AI venture capital, reflecting scalability and data limitations rather than a lack of need. Startups remain concentrated in high-income countries, and founding teams are predominantly technical and business-oriented, with limited clinical representation and gender diversity. By linking these empirical patterns to the five-tier framework, we show how AI systems complexity shapes innovation pathways, offering a foundation for more equitable, evidence-driven digital-medicine ecosystems.
Date Issued
2026-06-16
Date Acceptance
2026-03-20
Citation
npj Digital Medicine, 2026, 9
ISSN
2398-6352
Publisher
Nature Portfolio
Journal / Book Title
npj Digital Medicine
Volume
9
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41974803
PII: 10.1038/s41746-026-02595-5
Publication Status
Published
Coverage Spatial
England
Article Number
458
Date Publish Online
2026-04-14