AI agents in healthcare are intelligent, autonomous systems that assist clinicians, automate routine work, and personalize patient care by providing data-driven insights, improving diagnostic accuracy, and improving both operational efficiency and patient assistance.
We explained previously AI use cases in healthcare. This article lists healthcare AI agents that automate workflows in clinical operations.
Explore AI agents in healthcare, including tools used to general tasks, patient-facing supportAnd clinically assisted decision making:
AI Agents in Healthcare
General purpose health workers
These agents automate administrative and operational tasks (e.g., scheduling, medical coding, and office operations). They do not provide diagnostics.
Sully.ai
Sully.ai provides an agentic architecture for intake, coding, billing, and triage, with a focus on modular AI agents. Automates documentation, intake, scheduling and administration tasks.
Main features:
- Voice-action functionality: Translates physician speech into EMR actions using voice recognition.
- HIPAA Compliant: Ensures data handling and processing complies with HIPAA standards.
- Multilingual capabilities: Supports 19 languages.
Examples of Sully.ai AI agents:
Real Use Case: CityHealth Automates Healthcare with Sully.ai
CityHealth integrates Sully.ai’s AI healthcare platform directly into its electronic health records (EMR) to reduce time spent on patient care.
Automated Sully.ai:
- medical documentation
- reduces the need for manual changes
- allowed real-time data entry during consultations.
Results:
- Approximately 3 hours/day saved per clinician due to reduced record creation time
- 50% reduction in operations per patient
AI Beam
Beam AI offers a multi-agent system for healthcare management to automate medical record keeping, healthcare billing, medical compliance, patient appointment scheduling, and more.
Examples of Beam AI health workers:
Real-World Use Case: Avi Medical Automates Healthcare and Customer Service with Beam AI
Avi Medical has partnered with Beam AI to deploy multilingual AI agents. Beam agents retrieved relevant data from databases to respond to complex customer queries. Thanks to the ability of agents to access external data via APIs. AI agents handled a large number of routine requests (70% of tickets).
Results:
- 80% of patient inquiries have been automated
- 90% reduction in median response time
- 10% increase in Net Promoter Score (NPS)
Innovator
Source: Innovaccer
Innovaccer offers a suite of AI agents focused on value-based care and operations. Its agents support decision-making, not diagnosis.
Examples of Innovacer health workers:
Real Use Case: Franciscan Alliance Streamlines Coding with Innovaccer
Indiana-based multi-specialty physician network Franciscan Alliance uses Innovaccer’s platform to automate coding processes.
Results:
- Innovaccer’s physician engagement solution helped streamline coding processes, resulting in approximately a 5% improvement in reducing coding gaps.
- Automated protocols reduced the expected number of patient cases from about 2,600 to about 1,600.
Remarkable health
Notable Health uses AI agents to automate administrative tasks such as patient registration, appointment scheduling, referrals, care authorization and coding, all integrated into EHRs.
Real Use Case: North Kansas City Hospital Automates Patient Appointments with Notable
North Kansas City Hospital (NKCH) has been experiencing inefficiencies in patient registration and check-in. NKCH has partnered with Notable to automate various administrative workflows such as vaccine scheduling.
Results:
- Reduced patient check-in time by over 90% (from 4 minutes to 10 seconds).
- 80% of patients were pre-registered, compared to 40% previously.
Clinically augmented assistants
These systems help clinicians with analysis and prioritization. They do not replace medical judgment.
Hippocratic AI
Hippocratic AI is a healthcare-focused artificial intelligence company that developed the first large language model (LLM) specifically for non-diagnostic clinical (e.g., patient engagement, follow-ups, insurance coordination) and patient-facing tasks.
The company recently secured $141 million at a valuation of $1.64 billion.
Examples of Hippocrates AI agents:
Real Use Case: Partnership between WellSpan Health and Hippocratic AI
WellSpan Health has partnered with Hippocratic AI to launch a GenAI health agent that manages patient engagement calls. These agents can contact Spanish- and English-speaking patients, address their health needs and schedule screenings.
Result:
- The system allowed WellSpan Health to contact more than 100 patients, improving access to critical cancer screenings.
Patient support agents
These officers specialize in interacting directly with patients, answering questions, providing instructions, scheduling, and offering emotional support.
Amélie AI
Amelia AI agents can guide patients through their care journey. They can schedule appointments, answer patient questions, and provide empathetic conversational responses.
Real Use Case: Aveanna Healthcare Uses Amelia Agents for Customer Support
Aveanna uses Amelia AI Agent to manage repetitive employee interactions across Workday and mobile apps. The agent now handles password resets, user authentication, and other HR-related tasks.
Results:
- 560+ daily employee conversations managed by AI agent
- 95% of employee inquiries were resolved via Workday Chat.
Cognigie
Cognigy Agents are conversational AI agents for healthcare, providing assistance with insurance claims, prescription refills, and post-treatment care instructions.
Cognigy offers more than 30 ready-to-use voice and digital channels, from iMessage to WhatsApp and Twitter.
Use cases of Cognigy AI Agent for healthcare:
- ID&V (identity verification)
- Make and change appointments
- Medical Billing
- Update insurance
- Digital admissions process (submit personal and medical information digitally)
- Top-up requests
Real Use Case: Personify Pulse Maintains 40% Containment Rate with Cognigy
Personify Pulse implements Cognigy’s tool and integrates it with Zendesk LiveChat to process customer requests.
Results:
- Containment rate: Cognigy’s AI agent processed 40% of customer requests without human intervention.
- Automated ticket creation: The system automatically created support tickets, streamlining the tracking process.
Are health AI agents really agents?
At their base, AI Agents perform tasks, make decisions and take actions without the need for human assistance.
→ For the moment, health workers are not completely autonomous; most still require “humans in the loop” to perform tasks.
→ However, these agents possess several agentic capacities, in particular:
- Autonomous data recovery: Retrieve patient data from the system, including personal details and medical history.
- Data validation and accuracy verification: Verify data accuracy with existing records.
- Autonomous data validation and problem reporting: Validate verified data and report discrepancies for resolution.
- Autonomous data updating and record management: Update the patient file with validated information.
Will health AI agents become completely autonomous?
What we are seeing in today’s healthcare AI agents is “supervised autonomy“, where AI handles the heavy lifting of research (e.g., extracting data from lab reports) and repetitive tasks (e.g., record the patient’s vital signs), but with human oversight at key decision points.
These agents are still far from providing fully autonomous, production-ready results in complex medical use cases, such as patient placement and image scanning.
In the future, these systems could evolve into multi-agent networks, where different AI agents collaborate and interact, gradually upgrading to larger networks. agentic solutions.
For example, technology companies like NVIDIA and GE HealthCare are collaborating to create robotic agent systems such as X-ray and ultrasound, which use medical imaging to operate in the physical world.
Further reading
Senior Analyst
Cem Dilmegani
Senior Analyst
Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global corporations like Deloitte, HPE and NGOs like the World Economic Forum and supranational organizations like the European Commission. You may see more reputable companies and resources referencing AIMultiple.
Throughout his career, Cem has been a technology consultant, technology buyer and technology entrepreneur. He advised companies on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement for a telecommunications company while reporting to the CEO. He also led the business growth of deep tech company Hypatos, which achieved 7-figure annual recurring revenue and a 9-figure valuation from 0 in 2 years. Cem’s work at Hypatos has been covered by leading technology publications such as TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.