Artificial intelligence (AI) continued to rapidly advance and increase its capabilities in 2025. Heading into 2026, applications are being developed for nearly every industry, and the healthcare industry is no different.
AI in healthcare has expanded its offerings, from a beginning in ambient listening to more recent agentic AI for revenue cycle management (RCM). Even our AI-powered solutions have come a long way. As this technology continues to advance, it can be easy to get lost. That is why we have developed this introduction to AI in the healthcare space to help answer common questions that people in healthcare may have about AI and how it impacts them.
Understanding Artificial Intelligence in Healthcare
What is artificial intelligence (AI) in healthcare?
There are several different types of AI currently being used, from large language models (LLMs) that generate content and respond to you directly, to traditional AI that is rules-based and task-specific. These can be used to summarize and create clinical notes, automate administrative work, support decision-making, and improve overall efficiency.
What is the difference between AI, machine learning, and automation?
- Automation relieves users of manual processes by being supplied with rules (e.g., if X, then do Y).
- Machine learning: learns patterns from input data and makes predictions.
- AI is the broader term that includes machine learning, natural language processing (NLP), and generative models that can read and create text.
What is generative AI?
Generative AI uses large language models (LLMs) trained on a large amount of text to understand inputs and generate human-like responses. In healthcare, drafted responses can be clinical notes, patient history summaries, patient communication, or actionable insights and data.
What is agentic AI? How is agentic AI different from generative AI?
- Generative AI uses LLMs to understand context from prompts to produce content.
- Agentic AI can take actions with that content using various tools and software.
An example of this in healthcare would be an ambient AI generating a SOAP note from a patient-provider interaction, and an AI agent taking that note and using it to suggest ICD-10 codes and scheduling follow-up appointments.
What is natural language processing (NLP)? How does NLP work in healthcare?
Natural language processing enables programs to understand and interpret human language. This is a key component of large language models (LLMs).
NLP is used in healthcare for dictation, SOAP note generation, chart summarization, and coding suggestions.
Everyday Use Cases for AI in Medical Practices
How is AI used in medical practices today?
AI is beginning to be used at nearly every stage of medical practice workflows today.
Current AI uses in healthcare:
- Ambient clinical AI documentation
- Summarizing chart notes
- ICD-10 code generation
- Patient messaging automation
- Denial analysis and prevention
- Appeal package generation
- Visit intake automation
How does AI reduce provider documentation time?
AI, like ambient clinical AI, can reduce provider documentation time simply by listening to a patient visit and converting speech into structured documentation, suggesting diagnosis codes, and automatically summarizing encounters and patient histories. This can reduce provider documentation time by hours every day.
Can AI help improve patient care?
AI can help improve patient care by supporting clinical decision-making through improved documentation quality with increased accuracy and providing timely, relevant insights. It can also provide quick and easy access to your practice in the form of AI-powered call bots.
How does AI streamline scheduling, intake, and front-office workflows?
Generative and Agentic AI for healthcare operations can automate mundane tasks that usually fall upon front-office staff, freeing their time and streamlining scheduling, intake, and other front-office workflows.
AI can streamline front-office workflows by:
- Predicting appointment no-shows
- Automating reminders
- Streamlining insurance verification
- Pre-filling intake forms
- Routing patient messages to the right staff
- Assisting with triage for urgent vs non-urgent communication
Can AI help reduce physician burnout?
Yes.
With documentation being such a significant contributor to burnout for physicians, clinical AI powered by ambient listening that can reduce charting time by hours a day definitely helps clinicians regain work-life balance and reduce burnout.
Addressing Common Concerns About AI
Will AI replace doctors or healthcare staff?
No, AI will not replace doctors or healthcare staff.
AI augments clinical roles by handling repetitive and mundane administrative work, freeing healthcare staff to tend to patient care. Providers remain responsible for diagnosis, critical thinking, patient relationships, and medical decisions.
Is AI accurate enough for clinical settings?
AI can be highly accurate when used appropriately, especially for administrative tasks. Combined with the clinical knowledge and expertise of licensed physicians, AI can be a powerful tool for optimizing administrative workflows.
Can AI make medical errors?
Yes, AI can make errors. This is why clinicians must review AI-generated content and apply their personal knowledge and experience.
Privacy, Security, and HIPAA Considerations
Is healthcare AI HIPAA-compliant?
AI can be HIPAA-compliant if:
- It uses secure, encrypted systems
- It does not retain patient data
- BAAs are in place
- Data is isolated and protected
Not all AI vendors meet this standard.
How is patient data protected when AI is used?
When AI is used, patient data is protected through:
- Data encryption
- Strict access controls
- Zero-retention policies
- Logging and auditing
- HIPAA-compliant infrastructure
- Vendor BAAs
Does AI store or learn from my patient data?
Enterprise healthcare AI systems typically do not store or train on PHI. The best healthcare AI systems will openly tell you if this is the case.
Consumer-grade AI tools might use PHI to train, which is why contracting through compliant vendors is crucial for healthcare organizations to protect patient data security.
Getting Started with AI in Your Practice
How can our clinic safely adopt AI tools?
Healthcare practices can begin to safely adopt efficiency-driving AI tools with the following process:
- Evaluate your organization's low-risk administrative tasks and staff needs
- Identify HIPAA-compliant vendors
- Create an internal plan for best-practice policies
- Pilot with a small group first
- Train the remainder of the staff on incorporating AI into their workflows
- Monitor accuracy and impact
How much does AI cost?
Costs for healthcare AI vary by solution.
Ambient documentation and workflow automation tools typically have a monthly cost per provider or a cost per minute/encounter used, depending on capabilities and scale.
What questions should we ask AI vendors?
- Are you HIPAA-compliant?
- Do you retain or train on our data?
- Do you offer a BAA?
- How accurate is your system?
- How do you mitigate hallucinations?
- How do you integrate with our EHR?
- What is the total cost of ownership?
How do we train providers and staff?
Provide simple workflows, best practices, and examples.
Create clear “review before signing” guidelines.
Start with early adopters and expand gradually.
What next? Where do we go from here?
These questions and answers should give you a great base for understanding AI solutions in healthcare and how to implement them into your practice. Keep an eye out for our AI in Healthcare 201 for a more in-depth look at everything AI for health IT.
