Top 10 IA Agents in Healthcare with Examples

We list IA agents for healthcare that automate clinical operations workflows. Explore IA agents in the healthcare industry, including tools used for general tasks, EHR-native, patient-facing support, and clinically assisted decision-making:

IA agents in healthcare industry

General-purpose healthcare agents

These agents automate administrative and operational tasks (e.g., scheduling, medical coding, and office operations). They do not provide diagnoses.

Sully.ai

Sully.ai provides an agentic architecture across intake, coding, billing, and triage with a focus on modular IA agents. Automates documentation, intake, scheduling, and admin tasks.

Key features:

• Voice-to-action functionality: Translates physician speech into EMR actions using voice recognition.
  
• HIPAA compliant: Ensures that data handling and processing comply with HIPAA standards.
  
• Multilingual capabilities: Supports 19 languages.

Examples of Sully.ai’s IA agents:

Real life use case: CityHealth automates healthcare with Sully.ai

CityHealth integrates Sully.ai’s IA healthcare platform directly with its electronic medical records (EMRs) to reduce documentation time.

Sully.ai:

• drafted clinical documentation
• cut manual edits
• supported data entry during consultations.

Results:

• ~3 hours/day saved per clinician through reduced charting time
• 50 % decrease in operations per patient²
  

Beam AI

Beam AI offers a multi-agent system for healthcare management to automate medical record-keeping, healthcare billing, medical compliance, patient appointment scheduling, etc.

Examples of Beam AI healthcare agents:

Real-life use case: Avi Medical automates healthcare and customer service with Beam AI

Avi Medical partnered with Beam AI to deploy multilingual IA agents. Beam’s agents retrieved relevant data from databases to answer complex customer queries. Thanks to agents’ capability to access external data via APIs. IA agents handled high-volume, routine inquiries (70 % of tickets).

Results:

• 80 % of patient inquiries were automated
• 90 % reduction in median response time
• 10 % boost in Net Promoter Score (NPS)³

Notable Health

Notable Health uses IA agents to automate administrative tasks like patient registration, appointment scheduling, referrals, care authorization, and coding, all integrated with EHRs. 

Real-life use case: North Kansas City Hospital automates patient appointments with Notable

North Kansas City Hospital (NKCH) faced inefficiencies in patient check-ins and registration. NKCH partnered with Notable to automate various administrative workflows such as vaccine scheduling. 

Results:

• Over 90 % reduction in patient check-in time (from 4 minutes to 10 seconds).
• 80 % of patients were pre-registered, up from 40 %.⁴

EHR-native agents

EHR-native agents are artificial intelligence systems built directly into electronic health record platforms rather than added as external tools.

Epic

Epic covers roughly 42 % of the US acute care EHR market. At HIMSS26 in March 2026, Epic reported that more than 85 % of its client organizations use its IA features.⁵

Epic ships three named agents:

• Art drafts clinical notes, hospital course summaries, and nursing care plan text. At Mercy, average end-of-shift nursing note time fell from 3.5 minutes to about 32 seconds. Notes completed on time rose 225 %.
• Penny handles coding and denial appeals. Epic reports coding-related denials down more than 20 % at the sites using it most heavily, and appeal letters drafted 23 % faster.
• Emmie answers patient questions inside MyChart and handles scheduling. At Rush University Medical Center, billing-related service messages fell 58 %.

Oracle Health

Oracle Health Clinical IA Agent listens during a visit, drafts the note, and drafts the follow-up orders.

Oracle has widened the tool on three fronts: order drafting that covers labs, imaging, prescriptions, follow-ups, and referrals; note generation for inpatient and emergency settings; and coverage across 30+ specialties, with roughly 1 million notes generated.

Oracle reports close to a 30 % cut in daily physician documentation time. The tool also runs in the UK and Canada.⁶

Clinically augmented assistants

These systems assist clinicians with analysis and prioritization. They do not replace medical judgment.

Innovaccer 

Source: Innovaccer⁷

Innovaccer offers a suite of IA agents focused on value-based care and operations. Its agents support decision-making, not diagnosis.

Examples of Innovaccer healthcare agents:

Real-life 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 a ~5 % improvement in coding gap closure.
  
• Automated protocols reduced the expected number of patient cases from ~2 600 to ~1 600.⁸

Patient-facing support agents

These agents specialized in interacting directly with patients, answering questions, providing instructions, scheduling, and offering emotional support.

Hippocratic AI

Hippocratic AI is a healthcare-focused artificial intelligence company that developed the first Large Language Model (LLM) specifically for non-diagnostic (e.g., patient engagement, follow-ups, insurance coordination) and patient-facing clinical tasks. 

The company raised $126M at a $3,5B valuation, closed inNovember 2025, bringing total funding to $404M.⁹

Examples of Hippocratic AI’s agents:

Real-life use case: WellSpan Health and Hippocratic AI partnership

WellSpan Health partnered with Hippocratic AI to launch a GenAI healthcare agent that handles patient engagement calls. These agents can contact Spanish-speaking and English-speaking patients, address their health needs and schedule screenings. 

Result:

• The system enabled WellSpan Health to contact over 100 patients, improving access to critical cancer screenings.¹⁰

Amelia IA

Amelia IA Agents can guide patients through their care journey. They can schedule appointments, answer patient queries, and provide empathetic conversational responses.

Real-life use case: Aveanna Healthcare uses Amelia agents for customer support

Aveanna uses Amelia IA Agent to manage repetitive employee interactions via Workday and mobile apps. The agent now handles password resets, user authentication, and other HR-related tasks.

Results:

• 560+ daily employee conversations managed by the IA agent
  
• 95 % of employee requests were resolved through the Workday chat.¹¹

Cognigy

Cognigy’s agents are conversational IA agents for healthcare, providing support with insurance claims, prescription refills, and post-treatment care instructions.

Cognigy offers 30+ voice and digital channels out-of-the-box, from iMessage to WhatsApp and X.

Cognigy IA Agent use cases for healthcare:

• ID&V (Identity Verification)
• Make & change appointments
• Medical billing
• Update insurance
• Digital intake process (submit personal and medical information digitally)
• Refill requests

NiCE completed its $955M acquisition of Cognigy in September 2025. Cognigy now sits inside NiCE’s CXone Mpower platform.¹²

Real life use case: Personify Pulse maintains 40 % containment rate with Cognigy

Personify Pulse implements Cognigy’s tool and integrates it with Zendesk LiveChat to handle customer inquiries.

Results:

• Containment rate: Cognigy’s IA agent handled 40 % of customer inquiries without human intervention.
• Automated ticket creation: The system automatically created support tickets, streamlining the follow-up process.¹³

Amazon Health IA Assistant

Amazon launched an IA health assistant for Prime members in March 2026 that converses about symptoms, triages requests, schedules appointments, and connects to medical records.¹⁴

It is highly customizable and scalable within AWS ecosystem. IA health assistant requires integration and configuration.

Are healthcare IA agents truly agentic?

IA agents plan a task, call the systems they need, and act on the result. In healthcare, most stop short of acting without review

For now, healthcare agents are not fully autonomous; most still require ‘humans in the loop’ for task execution. Yet, these agents possess several agentic capabilities, including:

• Autonomous data retrieval: Retrieve patient data from the system, including personal details and medical history.
• Data validation and accuracy verification: Cross-check the data against existing records for accuracy.
• Autonomous data validation and issue flagging: Validate the verified data and flag discrepancies for resolution.
• Autonomous data updating and record management: Update the patient record with the validated information.

Ne manquez pas nos benchmarks et analyses basées sur les données. Le bouton ouvre Google ; sélectionner AIMultiple confirme que vous souhaitez voir AIMultiple plus souvent dans les résultats de recherche Google.

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Will healthcare IA agents become fully autonomous?

What we are seeing in today’s healthcare IA agents is “supervised autonomy,”  where IA handles the heavy lifting of research (e.g., data extraction from lab reports) and repetitive tasks (e.g., recording patient vital signs) execution, but with human oversight at key decision points. 

These agents are still far from delivering 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 IA agents collaborate and interact, gradually improving towards more agentic solutions.

For example, tech companies like NVIDIA and GE HealthCare collaborate to build agentic robotic systems like X-ray and ultrasound, which use medical imaging to operate in the physical world.¹⁵

Further reading

• Healthcare IA Use Cases with Examples
• Compare 5 Best IA Agents in Customer Service

Citer cette recherche

Choisissez le format qui correspond à votre lieu de publication. Coller la version avec lien dans votre CMS préserve le lien retour.

@misc{dilmegani2026,
  author = {Dilmegani, Cem and PhD., Ezgi Arslan,},
  title  = {{Top 10 IA Agents in Healthcare with Examples}},
  year   = {2026},
  month  = jul,
  howpublished    = {\url{https://aimultiple.com/ai-agents-in-healthcare}},
  note   = {AIMultiple. Consulté le 27 juillet 2026}
}

Journal des modifications

8 mises à jour

20262025

1. 27 juil

   Ajout d'une section sur les agents natifs des DSE couvrant Epic et Oracle Health.

2. 27 mar

   Ajouté Amazon Health AI Assistant à la section Cognigy.

3. 29 jan

   Suppression de la section « Exemples d'agents d'IA dans les soins de santé ».

Liens de référence

1.

AI Medical Scribe | AI Scribe - Sully

Sully.ai

2.

AI EMR Automation Reduces Burnout | Sully.AI - Sully.ai

Sully.ai

3.

Our Case Studies: AI Agents & Agentic Automation | Beam AI

4.

Notable Customer | North Kansas City and Meritas Health

Notable

5.

Epic grows its EHR footprint among small health systems: KLAS

Fierce Healthcare

6.

Oracle Health Adds Order Creation Capabilities to Oracle Health Clinical AI Agent to Support Accurate, Complete Records

7.

Agents of Care

8.

Franciscan Alliance’s Partnership with Innovaccer Yields $2.2 Million in Value — Innovaccer

Innovaccer

9.

Hippocratic AI Raises $126 Million in Series C at $3.5 Billion Valuation Led by Avenir Growth to Expand Clinically Safe Generative AI Agents Across Healthcare

Business Wire

10.

WellSpan one of the first to launch Hippocratic AI's Generative AI Healthcare Agent - Wellspan Health

11.

AI for Healthcare & Patient Access | SoundHound AI

SoundHound AI

12.

​​​NiCE Closes Acquisition of Cognigy | NiCE

NiCE

13.

Personify Health: 20x Containment Increase with Cognigy.AI

Cognigy

14.

Amazon's new Health AI Chatbot is rife with potential for misuse — here's why I wouldn't trust it with my data | TechRadar

TechRadar

15.

NVIDIA and GE HealthCare Collaborate to Advance the Development of Autonomous Diagnostic Imaging With Physical AI | NVIDIA Newsroom

Cem Dilmegani

Analyste principal

Suivre

Cem est analyste principal chez AIMultiple depuis 2017.

Le travail de Cem chez AIMultiple a été cité par des publications mondiales de premier plan, notamment Business Insider, Forbes, Morning Brew et Washington Post, par des entreprises mondiales comme Deloitte et HPE, par des ONG comme World Economic Forum et par des organisations supranationales comme European Commission. [1], [2], [3], [4], [5]

Tout au long de sa carrière, Cem a été consultant en technologies, acheteur de technologies et entrepreneur technologique. Il a conseillé des entreprises sur leurs décisions technologiques chez McKinsey & Company et Altman Solon pendant plus de dix ans. Il a également publié un rapport McKinsey sur la digitalisation.

Il a dirigé la stratégie technologique et les achats d'un opérateur télécom, sous la responsabilité du PDG. Il a également dirigé la croissance commerciale de l'entreprise de technologie profonde Hypatos, qui a atteint un revenu récurrent annuel à 7 chiffres et une valorisation à 9 chiffres, passant de 0 à ce résultat en deux ans. Le travail de Cem chez Hypatos a été couvert par des publications technologiques de premier plan comme TechCrunch et Business Insider.

Cem intervient régulièrement lors de conférences technologiques internationales. Il est diplômé de Bogazici University en tant qu'ingénieur informatique et titulaire d'un MBA de la Columbia Business School.

Voir le profil complet

Recherche effectuée par

Ezgi Arslan, PhD.

Analyste du secteur

Suivre

Ezgi est titulaire d'un doctorat en administration des affaires avec une spécialisation en finance et travaille comme analyste du secteur chez AIMultiple. Elle pilote la recherche et les analyses à l'intersection de la technologie et des affaires, avec une expertise couvrant la durabilité, les enquêtes et l'analyse des sentiments, les applications d'agents IA dans la finance, l'optimisation des moteurs de réponse, la gestion des pare-feu et les technologies d'approvisionnement.

Voir le profil complet

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