The State of AI In Healthcare: Patient and Provider Use Cases, Trust, and Best Practices

The use and perceptions of AI in healthcare has evolved dramatically across both the medical professional and patient experience within the past year. For healthcare providers, AI has moved from an experimental tool to a daily fixture directly embedded in workflows. For patients, AI has gone from a point of anxiety to an educational tool that patients are more likely to trust, whether themselves or their providers use it in a healthcare context.

Patients and their doctors are increasingly aligned on acceptable AI use cases in healthcare, including administrative automation and simplifying clinical language). They’re also aligned on its benefits, like increased time with patients, higher patient literacy, and better doctor-patient collaboration.) Patients and medical professionals also agree on the importance of having a human-in-the-loop, AI disclosure, and protecting patient data.

Still, patients and providers diverge in several key areas regarding the use of AI in healthcare.

They don’t use the same types of AI tools, they don’t align on how AI impacts provider authority, and they don’t use AI to achieve the same outcomes. Even the risks they’re most worried about are different, with patients most concerned with medical liability and accountability and clinicians most wary of hallucinations.

Finally, despite an increased usage of AI, healthcare hasn’t seen the resulting increases in CSAT and across-the-board ROI that other sectors have. The lower levels of AI autonomy in healthcare as compared to other sectors is certainly a factor here, as are stronger regulations and higher risks. The trust divide between different demographics when it comes to AI in healthcare also plays a role. But there’s an overlooked root cause for less transformational AI outcomes in healthcare than in other sectors: siloed data and disconnected systems.

This article outlines: 

• How the use of AI in healthcare is changing
• Popular AI use cases for patients and medical professionals
• How providers and patients differ in the way they use AI
• What’s driving the AI trust gap in healthcare
• Roadblocks to AI ROI and CSAT improvement in healthcare
• Best practices for using AI to improve the patient and provider experience 

The Rising Use Of and Trust In AI For Healthcare

Finding 1: The use of AI in healthcare is on the rise for patients and medical professionals, as is trust in its capabilities. AI healthcare providers no longer own the first patient interaction: AI models do. 

Within a single year, AI has moved from an experimental tool for doctors to a daily necessity in the workplace. From 2025 to 2026, the number of doctors using AI at least once a week nearly doubled, (growing from 38% to 74%), while the number of doctors using AI multiple times each day almost tripled (rising from 10% to 38%. At the same time, the number of doctors who say they “never” use AI at work fell from 43% in 2025 to 9% (Source 1). 

As patients become increasingly AI-fluent, with 81% saying they use AI tools at least once a week, their use of AI within a healthcare context is also growing. This increase in usage has shaped how today’s patients start healthcare interactions, with 50% of American consumers saying the first step of their patient journey begins with a public AI model, not a provider. Use of AI continues across their care journey, with over 40% of patients now bringing AI-powered healthcare insights directly into the exam room (Source 1).

As a result of higher general AI use, patients are now more trusting of AI use within the healthcare context.

Over 70% of patients now feel comfortable with AI drafting communications to insurance providers, handling appointment scheduling, and even with medical professionals using AI-powered listening technology to transcribe conversations and create clinical documentation.  Over half would use a healthcare service that begins with a voice AI interaction, and 65% are comfortable with using AI assessment tools before talking to a medical professional (Source 2). 

The shift to AI-first healthcare interactions changes the "digital front door” of the patient experience. Healthcare providers no longer own the first patient interaction: a public AI model does.

Providers can’t see their patient’s full conversation with that AI model, and they can’t share data collected by their AI scribe with the patient’s AI agent. This could change as the adoption of personal AI agents becomes more widespread, and as providers decide if and how their healthcare organizations will interact with them.

Until then, clinicians risk treating patients with incomplete or inaccurate information. They may also have to spend more appointment time “undoing” an AI-provided diagnosis that the patient is committed to. There’s also the risk of public-facing AI models slowly becoming a patient’s default healthcare advisor: one that controls whether or not a patient seeks treatment and the specific provider they seek it from.

Shared Healthcare AI Use Cases + Benefits For Patients and Clinicians

Finding 2: Medical professionals and patients use AI to simplify clinical jargon, increase patient literacy, and research medication interactions. Shared benefits include more time with patients, increased patient engagement, and proactive care.

While AI in healthcare is now much more common and accepted, use cases between patients and providers differ.

Patient and provider opinions of AI in healthcare overlap the most when it comes to the benefits it offers. 70% of patients and clinicians agree that AI enables greater patient literacy and engagement. Both also agree that AI is an effective tool for clinicians to summarize medical literature and seek care-related information (Source 1). 

One of the top mutual benefits of AI in healthcare is that it enables clinicians to spend more time directly interacting with their patients by handling low-stakes, routine tasks like appointment booking or patient intake. Another shared benefit is more proactive patients, with 26% of patients reporting that researching care via AI led them to seek medical care sooner than they otherwise would have.

There are several use case overlaps for AI in a healthcare context for providers and patients.

41% of doctors and 54% of patients use AI to research medication interactions. Both also use it to simplify medical jargon (43% of patients, 22% of doctors.) Finally, both also see AI as an effective tool to improve patient education: 36% of doctors use AI to generate educational materials like pamphlets or care guides, and over half of patients use it to learn more about their medical diagnosis or conditions. 

Where Patients and Providers Diverge in AI For Healthcare

Finding 3: Patients and providers use AI to accomplish different goals, use different AI tools, and differ in their beliefs regarding AI capabilities.  While providers mostly use AI for administrative tasks, patients mostly use it as a research and diagnostic tool.

In spite of the overlap between providers and patients, both groups use AI to accomplish different goals. For patients, the goal is generally informational: AI is used to “decode” complex healthcare jargon. For providers, the goal is workflow efficiency: AI reduces documentation and scheduling efforts, drafts patient communications, and handles other administrative tasks. 

Administrative tasks aren't nearly as popular with patients as they are with doctors. 

Patients report low interest in using AI for tasks requiring personal health data. Only 30% of patients report using AI to find a provider, and only 19% of patients use AI to navigate insurance coverage.

The two also differ in the types of AI tools they use.

Medical professionals rely on specialized, clinical-grade tools like EMR-integrated AI and validated medical reference databases (UpToDate, PubMed, etc.) Patients rely on general consumer LLMs (Google Gemini, ChatGPT, etc.)

This creates a structural disconnect, as the AI tools patients use to prepare for a visit have zero connection to the AI their providers use to deliver care. Although patients would likely hand over administrative tasks to an AI agent that sits inside a trusted, connected system, very few health systems today offer one. That's the opening for providers.

The most alarming divergence is how AI impacts patient perception: 49% of global consumers believe an AI-fluent person without medical training can perform at least one medical task as well as or better than a doctor.

This represents a real shift in the doctor-patient dynamic. Clinical authority is no longer assumed, it must now be earned in conversation with a patient who has already consulted AI. Patients today want their doctor to be the expert checker on what AI told them. Providers who lean into that validator role will build stronger relationships, while those who dismiss patient AI research could lose those patients to public LLMs.

Trust Gaps In AI For Healthcare: Top Concerns For Patients and Providers

Finding 4: Though overlaps exist, top concerns about AI use in healthcare vary between patients and medical professionals. While patients are most concerned with AI transparency and data protection, providers are most concerned with professional penalties and deskilling.

While research does show that trust in healthcare AI tools is steadily rising, both patients and providers have overlapping and diverging concerns.

Patient Trust Gaps 

Patients have the lowest comfort with AI in healthcare within a clinical context, and are especially focused on harm recompensation and recourse if an AI agent goes rogue. 75% of patients say understanding who is held responsible if an AI agent is wrong or causes harm as their single greatest concern about AI in healthcare.

Of course, privacy and PHI leaks are also a huge patient concern: 74% of patients worry about health data privacy, while 71% worry about PHI being sold or leaked.

Patients also care deeply about AI disclosure: 72% of US adults say it is extremely or very important that healthcare providers explicitly inform them whenever AI is being used in their care.

However, transparency surrounding the use of AI in healthcare is currently in short supply. Only 16% of American adults say they’re aware their healthcare provider has used AI in their care (46% are unsure.)

That transparency gap currently extends to healthcare providers, as only 27% of clinicians say they’re aware of published AI governance policies at their workplace. 

Clinician Trust Gaps

For clinicians, the biggest concerns surrounding the use of AI in healthcare are related to compliance penalties, bias, and deskilling.

77% of doctors fear an over-reliance will erode their clinical critical thinking skills and that ad-sponsored AI tools could introduce bias.

Clinicians also share the same privacy concerns as their patients, with 49% of physicians advocating for strict legal consequences and heavy penalty for improper AI use or data breach failures. 

Roadblocks to AI ROI and Increased CSAT In Healthcare

Finding 5: Overall AI ROI and CSAT gains lag behind in healthcare compared to other sectors. Poor system integrations, an inability to define financial impact, high patient frustration rates, siloed data, legacy systems, and increased risks are to blame.

Although AI use in healthcare is increasingly commonplace, the sector still lags behind in ROI. Only 18% of healthcare AI adopters report CSAT gains, compared to 42% market-wide. Clinical decision-support tools yield lower returns (2.9x for providers and 2.3x for pharma), with only 4% of clinical tools running autonomously.

On the other hand, back-office AI functions in healthcare, like revenue cycle and claims management, yield up to a 4x return on investment, with up to 67% of tools running as semi- or entirely autonomous.

So, what’s causing the AI ROI and CSAT roadblocks in healthcare?

First, poor integration and the resulting siloed data (9% in healthcare vs 5% in the overall market.) Healthcare providers often attempt to layer front-end AI bots over disconnected EMRs, legacy phone systems, and unintegrated portals. When an AI agent cannot access back-office systems to resolve a query end-to-end, it becomes an added barrier rather than a solution.

Additionally, healthcare stands out across all industries for having the highest rates of customer and patient frustration: usually caused by AI breakdowns, failing handoffs, and lack of context.

Unlike retail or travel, where a bot mistake causes minor friction, a breakdown in healthcare carries immediate clinical, privacy, and regulatory costs.

Best Practices To Improve The Patient and Provider Experience 

Finding 6: Re-architecting the data foundation, improving AI-human handoffs, adopting transparent workplace governance, establishing clear metrics, and mitigating risk are all effective ways to improve the patient and provider experience in healthcare. 

To improve patient and provider experience – and to speed up AI ROI in healthcare – follow the below best practices: 

Re-architect the Data Foundation

Businesses with connected contextual data can reclaim up to 10% of staff capacity previously wasted on tool switching and manual data re-entry.

Your goal is to build a centralized data layer so AI agents and human staff share a single source of trust.

Begin by replacing standalone point-solution bots with enterprise agentic platforms that directly connect to your scheduling software, RCM engines, and EMRs (Epic, MyChart, etc.)

Consolidate unscalable applications, and instead partner with specialized, healthcare-native platforms that handle ongoing model maintenance and workflow interaction.

Close the CSAT Handoff Gap

Businesses using context-rich, memory-enabled AI are two times more likely to boost CSAT and resolve over 40% of queries autonomously.

To eliminate patient frustration when handing off conversations from an AI agent to a human one, enforce persistent journey memory. Ensure 100% of interaction history, intent, and steps already taken by AI travel automatically with the patient when escalating or switching channels.

Explicitly design handoffs for high-stakes moments in the customer journey. While AI is useful for intake and context gathering, establish clear, friction-free human escape hatches.  

Build Transparent Workplace Governance Strategies

40% of clinicians report encountering unauthorized shadow AI tools in their workplace, opening up both patients and practices to serious risks.

To avoid this, create a centralized governance committee in your healthcare organization that unifies IT, compliance, and clinical leadership to oversee AI security and policy enforcement. 

Sanction workplace-approved tools to prevent shadow AI use. Replace riskier consumer-facing LLMs by providing clinicians with EMR-integrated ambient AI scribes and other validated tools.

Conduct mandatory training that clarifies unapproved vs approved tools, HIPAA compliance boundaries, and output verification protocols with all team members.

Establish Executive Measurements 

Require every AI initiative to provide measurable operational value within 90 days. If the investment doesn’t demonstrate a clear impact by then, terminate it.

Use conversational intelligence to automatically analyze 100% of captured patient interactions in real time, giving executives instant visibility into churn risks, recurring friction, and sentiment.

Expand scoreboards beyond deflection by transitioning from raw containment rates to outcome-based metrics, like First Contact Resolution, cost-per-contact, and full interaction quality scoring. 

Mitigate Legal, Regulatory, and Privacy Risks

Require clinical AI outputs to display explicit source material, citations, and decision trees directly onscreen.

Practice proactive patient disclosure by clearly telling patients when they’re speaking to an AI agent, and always make it easy for them to reach a real person. The same goes for note-taking, scheduling, or care triage. Always provide patient assurances on how Protected Health Information (PHI) is encrypted, anonymized, and never sold.

Designate both technical and clinical “first responders” equipped with kill switches and permission revocation tools to isolate faulty models or hallucinating agents before they impact patient care. 

Source 1:  Wolters Kluwer 2026 Future Ready Healthcare Survey Report

Source 2: PolyAI Healthcare Research Report 2026