Despite a surge of AI tools designed to assist doctors and patients, most Americans continue to rely on healthcare professionals for guidance and not AI in health insurance, even as Medicare, private insurers, and technology developers experiment with new payment models to integrate artificial intelligence into clinical care.
While AI promises faster diagnoses, predictive analytics, and streamlined administrative processes, its adoption in the US healthcare system remains uneven.
According to a recent Gallup report, 73% of adults still turn to doctors for medical information, with just 16% consulting AI chatbots, underscoring the persistent trust in human expertise even as AI in health insurance tools expand.
“Despite emerging technologies promising access to health information, the doctor is still the most trusted source,” Gallup notes.
Can AI Make Health Insurance Better?
Medicare, the nation’s largest single payer, plays an oversized role in shaping AI use cases in health insurance, as private insurers often follow its lead. Coverage requires a service to meet three criteria: fall into an existing benefit category, not be excluded by law, and meet the “reasonable and necessary” standard.
The challenge is that AI-enabled tools, particularly software-as-a-service (SaaS) platforms that assist with diagnostics or clinical decision-making, often don’t fit neatly into these categories.
Fractional Flow Reserve derived from Computed Tomography (FFR-CT), a tool that uses augmented software to help diagnose heart disease, was the first AI-enabled SaaS solution reimbursed by Medicare, categorized under diagnostic tests. Other AI applications, such as tools predicting cardiovascular risk or detecting diabetic retinopathy, have also received reimbursement.
However, many remain in temporary Category III coding, limiting payment consistency. CMS relies on MACs to set payment rates on a case-by-case basis, creating geographic variation in reimbursement and slowing widespread adoption. Emerging solutions such as AI modifiers in medical billing demonstrate the potential to standardize these processes across the system.
Aligning Incentives with Outcomes
Emerging payment models, such as the Advancing Chronic Care with Effective, Scalable Solutions (ACCESS) program, aim to reward outcomes rather than individual services. Participating organizations receive recurring payments tied to clinical improvement, allowing integrated care with AI health insurance tools.
These initiatives highlight opportunities to better align financial incentives with patient outcomes and expand access to clinically meaningful AI solutions. Additionally, predictive analytics in health insurance can help insurers anticipate high-risk patients and tailor interventions proactively.
Patients Still Trust People Over AI Health Insurance
Gallup’s survey revealed that most Americans still rely heavily on traditional medical sources. Fifty-three percent use websites endorsed by medical authorities, and nearly three-quarters consult doctors directly. Only a small minority of patients, grouped as “Health Self-Navigators,” integrate AI tools like ChatGPT Healthcare or Claude for Healthcare into their routine. Even within this tech-savvy cohort, 74% still turn to their doctors for guidance.
Experts emphasize the complementary role AI can play, particularly in administrative efficiency and supporting clinician decision-making. AI medical billing systems and insurance algorithms are increasingly used to optimize workflows, reduce errors, and enhance fraud detection.
In addition, AI algorithms used to detect insurance fraud help safeguard payer and patient interests, while AI and Insurtech collaborations continue to explore innovative applications.
As technology continues to evolve, Centers for Medicare & Medicaid Services (CMS) and other payers face fundamental questions: How should services delivered by autonomous AI be valued? Should AI replace some aspects of clinician labor in billing models? Addressing these issues will determine whether AI in health insurance becomes a reliable partner in care or remains a supplemental tool, used selectively and cautiously.
By integrating AI use cases in health insurance and leveraging insurance algorithms, providers and insurers can achieve more accurate pricing, faster claims processing, and better patient outcomes, illustrating a future where AI in health insurance is central to both efficiency and care quality.
Inside Telecom provides you with an extensive list of content covering all aspects of the Tech industry. Keep an eye on our Medtech section to stay informed and updated with our daily articles.