From Prioritization to Proof: The Capability–Deployment Gap in Digital Health and Medical AI

Authors

  • Andreas Keck, MD Syte Institute, Hamburg, Germany
  • Jefferson Fernandes, MD, PhD, MBA Federal University of Health Sciences of Porto Alegre, Porto Alegre, Brazil; THMT editor https://orcid.org/0009-0003-5031-8439

DOI:

https://doi.org/10.30953/thmt.v11.744

Keywords:

AI governance, clinical deployment, digital health, medical artificial intelligence, organizational readiness

Abstract

Drawing on findings from a survey of multinational pharmaceutical companies operating in Brazil and recent evidence on agentic and conversational medical AI, this editorial examines the capability–deployment gap: the distance between prioritizing technological capabilities and integrating them into routine, accountable, and sustainable practice. 

Downloads

Download data is not yet available.

References

1. Fernandes JG, Keck A. Digital health: a market survey of multinational companies and lessons from global peers. Telehealth Med Today. 2026;11:689. doi:10.30953/thmt.v11.689.

2. Deloitte AI Institute. State of AI in the enterprise 2026: the untapped edge [Internet]. Deloitte; 2026 [cited 2026 Jul 20]. Available from: https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html

3. Ferber D, Hilgers L, Höper C, Kinny-Köster B, Eckardt JN, Egger-Heidrich K, et al. Towards autonomous medical artificial intelligence agents. Nature. 2026 Jun 17. doi:10.1038/s41586-026-10675-5.

4. Liévin V, Palepu A, Weng WH, Saab K, Stutz D, Cheng Y, et al. Towards conversational AI for disease management. Nature. 2026 Jun 17. doi:10.1038/s41586-026-10764-5.

5. Topol E. Agentic AI comes to medicine. Ground Truths [Internet]. 2026 Jun 17 [cited 2026 Jul 20]. Available from: https://erictopol.substack.com/p/agentic-ai-comes-to-medicine

6. Stanford Institute for Human-Centered Artificial Intelligence. The 2026 AI Index report [Internet]. Stanford University; 2026 [cited 2026 Jul 20]. Available from: https://hai.stanford.edu/ai-index/2026-ai-index-report

7. Chen SF, Alyakin A, Seas A, Yang E, Choi JJ, Lee JV, et al. LLM-assisted systematic review of large language models in clinical medicine. Nat Med. 2026;32:1152-9. doi:10.1038/s41591-026-04229-5.

8. Microsoft. Use Microsoft Purview to manage data security and compliance for ChatGPT Enterprise [Internet]. Microsoft Learn; 2026 [cited 2026 Jul 20]. Available from: https://learn.microsoft.com/

9. Organisation for Economic Co-operation and Development. Governing with artificial intelligence: the state of play and way forward in core government functions. Paris: OECD Publishing; 2025. doi:10.1787/795de142-en.

10. Gretscher P, Keck A, Wolff J, König A, Graf-Vlachy L. Strategische Herausforderungen für etablierte Unternehmen im Zeitalter der generativen KI. In: Obermaier R, editor. Handbuch Industrie 4.0 und digitale Transformation. Wiesbaden: Springer Gabler; 2026. doi:10.1007/978-3-658-36874-6_26-1.

11. Wolff J, Pauling J, Keck A, Baumbach J. The economic impact of artificial intelligence in health care: systematic review. J Med Internet Res. 2020;22(2). doi:10.2196/16866.

Keck A, Wolff J, Gretscher P. From potential to proof: how artificial intelligence in healthcare now delivers measurable return on investment—and why many companies risk falling behind. In: Kilic A, editor. Artificial intelligence and machine learning in healthcare. London: Academic Press/Elsevier; 2026. p. 197-206

Published

2026-09-30

How to Cite

Keck, MD, A., & Fernandes, MD, PhD, MBA, J. (2026). From Prioritization to Proof: The Capability–Deployment Gap in Digital Health and Medical AI. Telehealth and Medicine Today, 11(3). https://doi.org/10.30953/thmt.v11.744

Issue

Section

Opinions, Perspectives, Commentary