5 AI usage trends in healthcare for 2026

According to AI Adoption In Healthcare Is Surging: What A New Report Reveals, a study by Menlo Ventures and Morning Consult found that 22% of healthcare organizations have already implemented domain-specific AI tools, up from 3% in 2023 and 10% in 2024, with health systems leading at 27% adoption. This represents more than twice the rate of AI adoption in the broader economy, where just 9% of companies have adopted AI.

As we look ahead, five key trends are expected to reshape how healthcare organizations use AI, manage its risks, and integrate it into daily care delivery.

1. From shadow AI to formal governance frameworks

Shadow AI is the unauthorized use of AI tools by clinicians and staff. According to an August 2025 press release from the Healthcare Financial Management Association (HFMA), despite 88% of health systems using AI internally, only 18% have a governance structure and fully formed AI strategy. However, things are improving as nearly 70% of CFO respondents indicated some governance structure existed in 2025, up from just 40% in 2024. The same HFMA report found that more than 80% of health systems lack mature AI programs to adequately manage investments.

Healthcare organizations are establishing AI governance boards, creating "AI formularies" that list approved tools, and implementing compliance frameworks that balance innovation with patient safety. As HFMA notes, healthcare executives understand that good governance around AI builds community trust and ensures responsible and ethical use of information. Organizations are building formal compliance policies to address shadow AI risks while preserving safety and clinician-patient relationships. These governance structures will define which AI applications can be used, establish validation standards, mandate transparency in AI decision-making, and create accountability mechanisms when AI contributes to clinical decisions.

2. Clinical-grade AI becomes embedded in daily workflows

According to Heather Landi in "Kaiser Permanente rolls out Abridge's gen AI clinical tech across 40-hospital system," Kaiser Permanente deployed Abridge's ambient documentation technology across 40 hospitals and more than 600 medical offices in eight states and Washington, D.C., making it available to more than 24,000 doctors. Desiree Gandrup-Dupre, senior vice president of care delivery technology services at Kaiser Permanente, described it as the largest implementation to date of ambient listening technology. The AI converts patient-clinician conversations into structured clinical note drafts in real time and integrates into electronic health record systems, working across more than 14 languages and over 50 specialties. As Dr. Ramin Davidoff, executive medical director with the Southern California Permanente Medical Group, noted in Landi's article, reducing administrative tasks makes it easier for physicians to focus on patients and foster effective communication while meeting individual patient needs.

Healthcare organizations are prioritizing AI that reveals its reasoning, cites its sources, and allows clinicians to verify recommendations before acting on them. Landi reports that Kaiser Permanente's implementation requires patient consent, and doctors review all clinical notes before entering them into medical records. This transparency maintains clinical judgment and professional accountability.

3. Agentic AI transforms administrative operations

According to Healthcare IT News, the Assistant Secretary for Technology Policy/Office of the National Coordinator for Health IT (ASTP/ONC) found that AI for automated billing increased from 36% to 61% between 2023 and 2024, making it the fastest growing use case for predictive AI in hospitals. Overall, nearly 70% of all US hospitals were using predictive AI in 2024, with health system-affiliated hospitals leading at 86%. Healthcare IT News reports that hospitals using third-party or self-developed AI showed higher rates of billing automation (73%) compared to those using EHR-sourced AI (58%), and about 20% of healthcare workers spend more than 20 hours each month correcting billing mistakes, a challenge that AI automation aims to address.

The CMS Interoperability and Prior Authorization Final Rule changed how payers manage prior authorization workflows, with organizations that fail to adopt automated solutions facing challenges. Agentic AI systems can autonomously gather required documentation from multiple sources, validate information against payer requirements, identify missing elements, and even generate supporting clinical rationale.

4. Predictive analytics and real-time clinical Intelligence

Predictive analytics within electronic health records combines clinical and operational data to enable patient views and proactive decision-making. These systems continuously analyze patient data, vital signs, laboratory results, medication orders, clinical notes to identify subtle patterns that may escape human notice.

According to a release from the Mayo Clinic, a team has developed an AI system that can detect surgical site infections with high accuracy from patient-submitted postoperative wound photos. Published in the Annals of Surgery, the system uses a Vision Transformer model that achieved 94% accuracy in detecting surgical incisions and an 81% area under the curve in identifying infections, trained on over 20,000 images from more than 6,000 patients across nine Mayo Clinic hospitals. Dr. Cornelius Thiels, a hepatobiliary and pancreatic surgical oncologist and co-senior author, noted that the current manual process is time-consuming and can delay care, while the AI model can help triage images automatically, improving early detection and streamlining communication between patients and care teams. Mayo Clinic researchers say the technology could detect subtle signs of infection before they become visually apparent, allowing for earlier treatment, decreased morbidity and reduced costs. As Dr. Hala Muaddi, first author of the study, explained, this could mean faster reassurance or earlier problem identification for patients, while offering clinicians a way to prioritize attention to cases that need it most, especially in rural or resource-limited settings.

5. Interoperability and connected AI systems

According to Cleaning up healthcare data for the AI era, a recent Experian survey rated healthcare professionals' confidence in data quality at just 7.08 out of 10, with top concerns including duplicated work from data inconsistencies, incorrect patient details, and missed appointments. Derek Plansky, senior vice president of strategic governance at Health Gorilla, an interoperability company and qualified health information network (QHIN), explains that AI models are only as reliable as the data they're trained on, with incomplete or biased records skewing algorithms and generating unsafe recommendations at the point of care. When health data is standardized, normalized and deduplicated, AI can generate consistent high-quality insights that clinicians can trust. Plansky notes that interoperable patient records ensure accurate decision support, reduce unnecessary testing, and enable predictive models that anticipate risks before they materialize.

The Model Context Protocol represents one emerging standard that defines how AI systems, large language models, and agent-based applications connect with trusted knowledge sources. This protocol enables healthcare organizations to adopt best-of-breed AI platforms that address specific use cases while still fitting into core workflows and sharing information appropriately.

Interoperability challenges extend beyond technical standards to include data quality and governance. As Plansky notes, clear policies and standards are essential to ensure AI models are fed accurate, compliant and consistent information. Foundational elements for AI success include data that is clean, complete and high quality; good data governance; and validation of all processes that AI might touch.

As Plansky further notes that, interoperable data seamlessly stitches integration across electronic health records, laboratories and payer systems, creating coordinated and efficient care. Healthcare leaders emphasize being cloud-first and focusing on interoperability to be in a very different place a year from now.

FAQs

How will smaller or rural healthcare organizations afford AI adoption in 2026?

Smaller organizations are expected to rely on shared cloud platforms, vendor-hosted AI tools, and payer partnerships to reduce upfront infrastructure costs.

How will regulators evaluate AI systems that continuously learn and adapt?

Regulators are moving toward lifecycle-based oversight models that require ongoing monitoring rather than one-time approvals.

What skills will clinicians need as AI becomes embedded in workflows?

Clinicians will need AI literacy skills to interpret outputs, identify errors, and understand when to override automated recommendations.

How will healthcare organizations measure return on investment from AI tools?

ROI will be assessed through metrics like clinician time saved, reduced denials, improved patient outcomes, and lower administrative costs.