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Q4 2025 Healthcare AI Trends: Clinical Uptake Grows, Governance Tightens, 2026 Will Put AI Scalability to the Test

Hadeel Abu Baker

Senior Healthcare IT Consultant and Business Analyst, ScienceSoft

Vadim Belski

Head of AI, Principal Architect, ScienceSoft

Healthcare IT
Artificial Intelligence
Last updated:
6 min read

AI is becoming a mainstream investment in healthcare, but only where it can be effectively governed and scaled. Drawing on ScienceSoft’s Q4 2025 Healthcare AI Market Watch and over 20 years of experience in healthcare IT, we highlight where adoption is happening now, what providers and vendors should prepare for next, and why regulations, infrastructure, and scale-readiness will define 2026 outcomes.

Healthcare AI Trends

Key takeaways:

  • Clinical AI is moving into large-scale adoption. Ambient documentation leads the way, with decision support tools gaining ground.
  • Conversational agents are expanding beyond simple chatting to handle complex workflows and guide care.
  • Governance is catching up. Federal scrutiny is growing, states are resisting, and clinicians are gaining influence in AI design.
  • Many providers are prioritizing EHR upgrades, security, and interoperability of the core systems before expanding AI.

What 2025 AI Investments Reveal About the Healthcare Industry’s Next Moves

AI investment in healthcare shifted from trial to commitment in late 2025. The share of US healthcare organizations that had purchased AI licenses grew from 3% to 22% in just 2 years. Adoption is clearly picking up speed, and it’s no longer limited to experiments or pilots. ScienceSoft expects this trend to continue into 2026, with a growing preference for AI that’s deeply integrated into clinical and operational workflows.

Investment patterns tell the same story. AI-enabled digital health companies attracted 54% of total venture funding in 2025, up from 37% in 2024. They also raised bigger rounds: 19% larger, on average, than those of non-AI companies. The strongest interest went to products that fill a specific role in clinical or operational workflows, such as ambient clinical scribes, AI voice agents for appointment scheduling, and patient-facing assistants that help navigate care.

In Q4 2025, provider spending shifted decisively toward AI-native startups. About 85% of GenAI budgets went to young companies offering standalone tools purpose-built for specific tasks. Buyers are increasingly drawn to focused solutions that meet their needs without the infrastructure constraints of big platforms. In response, many growth-stage companies (e.g., New Mountain Capital, Abridge, Innovaccer) turned to acquisitions, with healthtech M&A rising by 61% in 2025.

“Bundling AI into larger enterprise suites changes what buyers have to evaluate,” says Hadeel Abu Baker, Senior Healthcare IT Consultant at ScienceSoft. “Providers are now realizing that when AI is tightly coupled to a bigger platform’s infrastructure, switching becomes expensive. That’s why focused tools are gaining ground: they solve a specific need, integrate through a limited set of APIs (e.g., FHIR-based), and are easier to replace if needed. In 2026, buyers will start asking practical questions: Can we export AI assets like prompts or logs? Can we change models or replace the tool without rebuilding surrounding systems? And can the AI fit naturally into day-to-day workflows without being hardwired into a single vendor’s stack? These criteria will matter just as much as features or price.”

 

Even as investment ramps up, part of the industry remains cautious. Forrester expects at least one major AI-related failure in 2026, serious enough to trigger financial or reputational fallout and push the industry to tighten expectations.

Ambient Scribes Lead Clinical AI’s Rise: 2026 Will Test Its Readiness to Scale

Ambient documentation tools became the most heavily funded AI category in 2025, marking a major turning point in clinical adoption. Until recently, most AI investment focused on lower-risk areas outside direct care: back-office functions like claims processing and supply chain management or patient-facing tools for scheduling and communication. Clinical decision support and diagnostic AI, in contrast, faced tighter scrutiny and stayed mostly in pilot mode, as noted in ScienceSoft’s Q3 2025 trend watch. In Q4, ambient scribing stood out as the first clinical AI category to see broad investment and deployment.

Among clinical AI tools, ambient scribes deliver the highest ROI with the lowest risk. They’re affordable, fit easily within existing workflows, and show measurable impact quickly. On top of that, every output can be reviewed before it’s entered into the record, so providers stay in control. That’s what helped ambient AI break through first.” — Vadim Belski, Head of AI and Principal Architect at ScienceSoft.

 

The US Department of Veterans Affairs is leading by example. After piloting ambient AI scribes in 10 medical centers, the Veterans Affairs Department saw early benefits: less time spent on documentation, clearer visit summaries for patients, and a stronger sense of personal connection during appointments. A full national rollout across its 170 medical centers and over 1,190 outpatient clinics is planned for 2026.

Private-sector momentum followed. A survey of 700+ healthcare executives showed spending on ambient scribes grew 2.4x in 2025. Vendors moved quickly to embed the tech: athenahealth introduced athenaAmbient as part of its AI-native encounter experience, while Altera launched Sunrise Thread AI, a native scribe integrated into the EHR.

Many assume integrations or workflow friction is the hardest part of ambient scribe rollouts. In reality, the speech layer often breaks first. Accents, dialects, and noisy environments can confuse base models, and fixing that requires fine-tuning, which means extra investment,” comments Vadim Belski, Head of AI and Principal Architect at ScienceSoft. “2026 is when ambient AI will be tested at full scale. Tools that worked in a small rollout will now have to hold up across hundreds or thousands of users. That’s when real issues show up: uneven adoption, edge cases nobody planned for, and quality drift between departments or sites. A good pilot is only the first step.”

 

As documentation tools gain ground, attention shifts to the next step: clinical decision support (CDS). 51% of health systems are already using AI for CDS in some form, according to a report by the Peterson Health Technology Institute. However, the survey doesn’t specify which CDS tools qualify as “AI” for purchasing decision-makers at health systems, so, according to consultants at ScienceSoft, this likely spans a wide range of capabilities and deployment maturity, from EHR-embedded alerts to pharmacy decision support and early pilot programs.

Meanwhile, some providers are moving AI clinical decision support further into higher-risk areas like surgery. For example, Cleveland Clinic is co-developing predictive modeling tools to assist physicians during TAVR procedures. These high-risk applications promise precision but require deep validation and clinical oversight to succeed.

Conversational AI Evolves: From Chatbot to Digital Front Door and to Clinical Coordination

Conversational AI is now taking action, not just answering questions. Typical voice assistants are evolving to multi-step agentic systems that can make decisions across patient access, administrative support, and clinical coordination.

Some applications act as “digital front doors,” helping patients understand and navigate care. Included Health’s assistant, Dot, can explain benefits, suggest care options, and help with appointment scheduling. It draws on real-time access to claims data and medical records, with clinician-in-the-loop oversight. Over the past year, Dot has handled thousands of member conversations and is now used by approximately 300 employers and health plans.

Other tools focus less on guidance and more on practical tasks. Yosi Health’s AI Voice Agent is already in use with early adopters. It can book live appointments from the EHR or practice management system, detect red flags like chest pain, verify insurance eligibility, and capture payments. It can also switch between phone calls, text messages, and web chat without losing the thread of the conversation.

The far end of the spectrum is guided care. TytoCare’s Smart Clinic Companion combines a home diagnostic device with clinical AI agents. It guides patients through physical exams, offers validated insights, and recommends next steps, whether that’s scheduling a visit or continuing self-management. A broader rollout is planned for February 2026.

ScienceSoft is also contributing to this shift,” says Vadim Belski, Head of AI and Principal Architect at ScienceSoft. “In a recent project for the Amazon Nova Partner Demo Competition, we turned a speech-to-speech model into an autonomous voice agent for medical appointment scheduling. The agent verifies patient identity, checks provider availability via FHIR-based EHR integration, and updates scheduling records in real time. The estimated outcomes showed a 40% reduction in booking time and a 50% cut in operational costs, and we were proud to see it named a semifinalist.”

Regulations Get Serious: Governance Now Shapes the Product

In late 2025, healthcare AI moved into a new phase, shaped as much by governance as by innovation. Regulatory efforts, once limited to general principles of AI safety and privacy, are now producing concrete rules that affect how AI tools are built, evaluated, and marketed.

At the national level, attention is tightening around higher-risk use cases. In Q4, the FDA’s Digital Health Advisory Committee met to examine how generative AI in digital mental health should be evaluated. They focused on lifecycle risk management, post-market monitoring, and balancing innovation with patient safety.

Tension is building between federal and state approaches. Donald Trump’s move to limit state-level AI regulations met pushback from local governments. For example, Illinois reaffirmed its intent to preserve local oversight, while California responded by passing Assembly Bill 489. The Bill prohibits AI tools from implying that they offer advice from a licensed medical professional. This raises new stakes for how consumer-facing systems can communicate.

Privacy rules are evolving around AI, too. The proposed Health Information Privacy Reform Act (HIPRA) aims to extend federal protections to data generated outside traditional healthcare settings. That includes wearables, wellness apps, and certain AI platforms that have operated with fewer constraints until now. If passed, HIPRA would impose new requirements for consent, data use, and breach notifications, forcing AI vendors to rework how they handle data that didn’t fall under HIPAA.

For many consumer health apps, HIPRA, if adopted, will force changes in how data moves through the product,” notes Hadeel Abu Baker, Senior Healthcare IT Consultant at ScienceSoft. “For example, if a wearable app uses sleep and heart-rate data for AI‑driven coaching, analytics, and marketing, those can’t sit in one shared pipeline anymore. You need purpose-based consent that’s enforced at runtime, so every service checks what the user allowed before data is used, sent to an AI model, or exported. In practice, that means classifying data at ingestion, splitting operational flows from analytics, reducing or moving third-party SDKs to controlled server-side flows, and building audit trails for access, exports, and model use. The level of effort will depend on how many downstream partners you have and how entangled your data lake is today.”

 

Beyond formal policy, industry groups are stepping up. The American Medical Association launched the Center for Digital Health and AI to ensure that physicians are involved in how new tools are developed and deployed. Its focus areas include workflow integration, patient safety, training, and ensuring that AI remains clinically relevant.

AI Needs a Stable Foundation to Scale Effectively

Despite strong interest in advanced AI applications, much of healthcare’s tech focus in 2025 remained on core infrastructure. Before AI can scale, organizations still need to modernize EHR systems, improve interoperability, secure data flows, and enable virtual care.

New York State offered a visible example. In 2025, it allocated over $300 million to upgrade digital infrastructure across 22 hospitals, with funding aimed at health IT modernization, telehealth capabilities, and cybersecurity. Nationwide, hospital and health system CIOs reported similar priorities. According to KLAS, most plan to maintain or increase health IT spending over the next 12 months.

Interest in virtual care and patient monitoring also remains strong. A Deloitte survey found that 60% of healthcare executives plan to invest in virtual health services in 2026, and 50% plan to fund tech-enabled engagement and monitoring tools. These tools often involve AI, but only after the supporting systems are in place.

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