Contributor: Artificial Intelligence Grows Across Health Care, Led by Administrative Processes

New findings from the 2025 CAQH Index¹ suggest that artificial intelligence (AI) adoption is increasing across health care, with nearly 60% of medical health plans, 50% of dental health plans, and 25% of providers reporting AI use in their workflows in 2025. Provider momentum is rapidly building, though, with an average growth of almost 40% since 2024. Industry implementation is concentrated in high-burden administrative workflows, including prior authorization, fraud detection, and documentation tasks, which include generating or translating visit notes, real-time note-taking, and ambient scribing.

These results indicate that health care is moving beyond discussion and beginning to apply AI in targeted operational areas. As organizations face ongoing pressure to improve efficiency and address the remaining $21 billion automation opportunity identified by the 2025 CAQH Index,² AI is increasingly being used to streamline existing processes and reduce manual work.

AI Usage

Adoption remains concentrated in use cases where organizations can more readily evaluate performance, manage risk, and demonstrate value. Amid regulatory uncertainty, cybersecurity concerns, implementation challenges, and increasing pressure to move beyond pilot programs, many organizations are prioritizing structured administrative workflows over more complex, clinical applications.^3-6 Administrative processes often involve standardized rules, established workflows, and clearly defined outcomes, making performance easier to evaluate than in more complex clinical settings.⁴

This pattern is particularly evident among health plans, where AI adoption is concentrated in some of health care’s most resource-intensive administrative functions. More than two-thirds of medical and dental plans use AI for prior authorization and fraud detection, reflecting a shared focus on interoperability and operational efficiency.^5,6 Other leading use cases vary by sector, with about one-third of medical plans using AI for attachments and claims payments and two-thirds of dental plans utilizing it to help optimize provider networks. Together, these applications support efforts to improve efficiency, strengthen accuracy, and manage utilization in a complex regulatory and cost environment.^7,8

Providers are pursuing a similar strategy, focusing AI use on labor-intensive administrative activities. Nearly half of medical providers and more than one-third of dental providers use AI for visit note generation and ambient scribing, helping reduce documentation burden and administrative contributors to burnout.⁹ Similar to health plans, providers are leveraging AI for prior authorization and pre-determination processes, with more than half of medical and dental providers reporting adoption. AI is also being used to verify patient eligibility and benefits by 47% of medical providers and 68% of dental providers. Since 2023, administrative AI use has changed modestly, increasing 5 percentage points on average among medical providers and decreasing 5 percentage points on average among dental providers.

The slight decline among dental providers aligns with increased adoption of clinical AI use, a trend that is considerably more pronounced in the dental industry than medical. Nearly half (44%) of dental providers report using AI to interpret diagnostic outcomes, such as x-rays or lab results, compared with 9% of medical providers. This suggests that AI is gaining traction in clinical settings where performance can be easily measured and validated.¹⁰

The gap becomes even clearer when examining clinical AI adoption overall. Among dental providers using AI, 58% report using it for clinical applications in 2025, up from 49% in 2024 and 22% in 2023. By comparison, clinical AI use among medical providers increased 32% in 2023 to 35% in 2025. Growing adoption of targeted dental applications, including radiographic imaging, orthodontics, and treatment planning, highlights how AI can support diagnostic accuracy and treatment planning in clinical workflows.^11-15

Regardless of organization type, experiences among those using AI were generally positive. Across multiple use cases, about 40% of plans and providers indicated that AI-supported processes were as accurate or more accurate than approaches not using AI. Positive perceptions were most common in areas involving structured workflows and clearly defined outputs, such as documentation tasks (eg, real-time note taking, ambient scribing), coding review, claims preparation, and provider contracting. These findings suggest that early adoption efforts are building confidence in AI’s ability to support operational improvement and workflow optimization.

As the industry moves forward, the results suggest that broader AI adoption will be driven by practical value rather than breakthrough innovations. Health care organizations continue to prioritize administrative workflows where burden is high, results are measurable, and efficiency gains are clear. Looking ahead, success will depend on expanding proven use cases, improving performance in more complex applications, and building the data and workflow foundations needed to scale AI adoption.

1. DataSpring, powered by CAQH. Industry Research. Accessed July 15, 2026. https://www.dataspring.com/advisory-services/industry-research
2. DataSpring, powered by CAQH. Index Report. Accessed July 15, 2026. https://www.dataspring.com/advisory-services/index-report
3. Perna G. How healthcare AI is regulated by the FDA, HHS, state laws. Modern Healthcare. March 26, 2024. Accessed July 15, 2026. https://www.modernhealthcare.com/digital-health/healthcare-ai-regulation-fda-hhs/
4. Mennella C, Maniscalco U, De Pietro G, Esposito M. Ethical and regulatory challenges of AI technologies in healthcare: a narrative review. Heliyon. 2024;10(4):e26297. doi:10.1016/j.heliyon.2024.e26297
5. Berryman L. Change Healthcare data breach: industry 'not fine' 1 year later. Modern Healthcare. February 19, 2025. Accessed July 15, 2026. https://www.modernhealthcare.com/providers/change-healthcare-data-breach-anniversary/
6. Famakinwa J. Tampa General, WellSpan, Vanderbilt share lessons on AI pilots. Modern Healthcare. June 16, 2026. Accessed July 15, 2026. https://www.modernhealthcare.com/health-tech/mh-tampa-general-wellspan-vanderbilt-ai-pilots/
7. Aldosari H. Artificial intelligence in healthcare administration and clinical informatics: a critical review and governance roadmap. Healthcare (Basel). 2026; 14(11):1497. doi:10.3390/healthcare14111497
8. 2024 CMS interoperability and prior authorization final rule (CMS-0057-F). Centers for Medicare & Medicaid Services. Updated July 21, 2026. Accessed July 15, 2026. https://www.cms.gov/initiatives/burden-reduction/overview/interoperability/policies-regulations/cms-interoperability-prior-authorization-final-rule-cms-0057-f
9. Anderson O. ADA urges dental-specific approach in CMS interoperability, prior authorization proposal. ADA News. June 12, 2026. Accessed July 15, 2026. https://adanews.ada.org/ada-news/2026/june/ada-urges-dentalspecificapproach-incms-interoperability-prior-authorization-proposal/
10. Minemyer P. Insurers have eliminated 11% of prior authorizations under reform pledge. Fierce Healthcare. April 7, 2026. Accessed July 15, 2026. https://www.fiercehealthcare.com/payers/insurers-have-eliminated-11-prior-authorizations-under-reform-pledge
11. Johnson JM, Khoshgoftaar TM. Data-centric AI for healthcare fraud detection. SN Comput Sci. 2023;4(4):389. doi:10.1007/s42979-023-01809-x
12. Moy AJ, Schwartz JM, Chen R, et al. Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc. 2021;28(5):998-1008. doi:10.1093/jamia/ocaa325
13. Iruvuri AG, Miryala G, Khan Y, et al. Revolutionizing dental imaging: a comprehensive study on the integration of artificial intelligence in dental and maxillofacial radiology. Cureus. 2023;15(12):e50292. doi:10.7759/cureus.50292
14. McAlpine KJ. AI may be just what the dentist ordered. Harvard Medical School. November 30, 2023. Accessed July 15, 2026. https://hms.harvard.edu/news/ai-may-be-just-what-dentist-ordered
15. Gordon D. AI would make people trust their dentist more, new survey shows. Forbes. December 11, 2023. Accessed July 15, 2026. https://www.forbes.com/sites/debgordon/2023/12/11/ai-would-make-people-trust-their-dentist-more-new-survey-shows/