ment.tech-How to Develop an AI Agent Healthcare App like Sully AI (1).pdf

How to develope an AI agent

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Ani Khandelwal August 21, 2025
How to Develop an AI Agent Healthcare App like Sully
AI?
ment.tech/ai-agent-healthcare-app-development-like-sully-ai
Artificial intelligence is becoming one of the most important forces in healthcare. The
global market for AI in healthcare was valued at more than 20 billion dollars in 2024 and
is expected to cross 180 billion dollars by 2030. This growth is being driven by the need to
improve clinical accuracy, reduce the burden of paperwork, and make care more
accessible. Hospitals and clinics are increasingly turning to AI healthcare apps to save
time, cut costs, and improve outcomes for both providers and patients.
Sully AI, also known as sully.ai—AI medical assistant, is a clear example of how this shift
is taking shape. By combining multiple AI agents into healthcare apps, it helps doctors
with scribing, nurses with patient follow-ups, and reception staff with scheduling. The
platform shows how artificial intelligence can handle the background work while clinicians
stay focused on treatment. Its simple design and seamless integration with electronic
health records make it widely adopted among healthcare organizations.
For anyone looking to create an AI healthcare app like Sully AI, the opportunity is
significant. Healthcare providers are actively searching for tools that reduce burnout,
improve efficiency, and keep them compliant with strict data standards. Building a system
that balances intelligent automation with an easy-to-use interface will set a successful AI
powered healthcare app apart in this rapidly expanding market.

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What is Sully AI?
Sully AI, or sully.ai—an AI medical assistant, is designed to take on the everyday tasks
that often slow down doctors, nurses, and support staff. Instead of juggling paperwork,
phone calls, and patient records, clinicians can rely on Sully to quietly manage the
background work. The system introduces a team of digital agents that work together,
each trained to handle a specific role in the patient journey.
For a new patient visit, Sully can guide the intake process, collect basic details, and
prepare the chart before the consultation begins. During the appointment, the AI scribe
listens in and produces structured clinical notes in real time. Once the visit ends, the
receptionist agent helps with scheduling the next appointment, while the nurse and
consultant agents provide follow-up instructions. The researcher agent supports clinicians
with evidence-based recommendations when needed.
What makes Sully stand out among other AI healthcare apps is how it fits into existing
hospital workflows. It does not ask providers to change their systems or learn new
software from scratch. Instead, it integrates directly with electronic health records and
adapts to the way each practice already works. This balance between automation and
familiarity is why so many organizations see it as a trusted AI powered healthcare app
that improves efficiency without disrupting care.
How Sully AI Earns Revenue and Why its Pricing Works?
Sully AI has built a pricing model that makes sense for both small clinics and large
healthcare networks. The plans are straightforward, flexible, and designed to scale with
an organization’s needs. Here is a breakdown of how it works:
Professional Plan: Around 79 dollars per provider each month, suitable for small
practices that want to reduce manual work and streamline daily operations.
Premium Plan: Around 99 dollars per provider each month, designed for larger
teams that require more advanced features and stronger workflow support.
Enterprise Plan: Custom pricing tailored to the size of the hospital or healthcare
network, including options for deep integration with existing systems.
Free Trial: A limited number of visits included each month, giving providers the
chance to experience the platform before committing.
This approach allows Sully to position itself as both accessible and scalable. The flexibility
of AI Development Company powers the entry barrier for smaller providers while giving
larger institutions the ability to expand without disruption. For anyone planning to create
an AI healthcare app like Sully AI, adopting a similar tiered model ensures that the
product can grow with the customer base and remain competitive in the market of AI
healthcare apps.

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Why Sully AI Stands Out in Healthcare?
The strength of Sully AI lies in how it solves real problems for healthcare providers.
Instead of offering just another app, it has proven to deliver measurable impact in different
clinical settings. A few examples show why it is trusted across the industry:
1. Reducing Documentation Fatigue
Doctors often spend hours after clinic hours completing patient notes. Clinics using
Sully.ai – AI medical assistant report that automated scribing saves them close to three
hours a day. That time goes directly back into seeing more patients or balancing personal
time, which reduces burnout significantly.
2. Improving Patient Flow
In busy urban hospitals, delays in scheduling and intake slow down the entire system.
With Sully’s intake and receptionist agents, providers have seen a faster turnaround of
patient visits and fewer appointment gaps. This translates into higher efficiency without
adding extra staff.
3. Seamless Fit with EHR Systems
Hospitals usually hesitate to adopt new tools because they fear disruption to existing
electronic health records. Sully’s ability to integrate smoothly with popular systems like
Epic and Cerner means clinics can adopt it without re-training staff. This is one of the
main reasons it has been welcomed by over 300 organizations.
4. Trust Through Compliance
Healthcare leaders are cautious about data privacy. Sully operates within HIPAA-
compliant frameworks, ensuring that patient information is always secure. By prioritizing
compliance from the beginning, it builds confidence not just with providers but also with
regulators and patients.
5. A Track Record of Measurable Gains
Organizations using Sully have reported efficiency improvements of up to 50 percent and
a major drop in clinician burnout. These are not theoretical numbers but outcomes
measured in active healthcare environments, making Sully one of the most trusted AI
healthcare apps on the market.
Why You Should Invest in Developing an AI Healthcare App like
Sully AI
The healthcare industry is moving quickly toward automation, and the demand for
intelligent tools continues to grow. Here are the key reasons why this is the right time to
step in:

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Explosive Market Growth: The market for AI healthcare apps was worth more than
500 million dollars in 2024 and is projected to reach nearly 5 billion dollars by 2030.
This rapid expansion is driven by the urgent need to reduce costs, improve
decision-making, and ease the burden on doctors and nurses.
Proven Success Stories: Sully AI has already shown what is possible, with more
than 300 healthcare organizations using the platform. Its success has attracted
major funding, proving the strong appetite for solutions like sully.ai – AI medical
assistant. Other companies, such as K Health with 300 million dollars raised and
Babylon Health with more than 600 million dollars, confirm the growing demand for
AI as a service companies.
Real Problems, Real Solutions: Providers are looking for tools that reduce
paperwork, speed up workflows, and improve accuracy. Choosing to develop an AI
healthcare app like Sully AI means directly addressing these challenges. An app
that makes clinicians’ lives easier and improves the patient experience has a much
higher chance of rapid adoption.
Strong Investor Confidence: Venture capital and healthcare investors are actively
backing this space. Platforms that demonstrate efficiency, compliance, and usability
attract both providers and financial support, which makes building an AI healthcare
app today a strong long-term opportunity.
The Role of AI, EHR, FHIR, and Other Technologies in Healthcare
Apps like Sully AI
When you set out to create an AI healthcare app like Sully AI, it is important to understand
the core technologies that make such platforms effective. Each layer of technology plays
a role in turning raw data into useful insights for clinicians and patients.
Artificial Intelligence in Healthcare Apps
AI is the engine that drives platforms like sully.ai – AI medical assistant. It is responsible
for listening, transcribing, analyzing, and assisting doctors during the clinical workflow.
Real-time scribing: The AI listens to doctor-patient conversations and converts
them into structured notes ready for electronic health records.
Speech recognition and dictation: Doctors can speak freely, while the system
transcribes accurately, saving hours of manual typing.

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Medical coding: Clinical notes are converted into billing codes, reducing errors and
speeding up claims.
Decision support: By analyzing patterns in patient data, the AI can suggest
possible actions or highlight risks a doctor might otherwise miss.
EHR Integration
Electronic Health Records (EHRs) sit at the center of modern healthcare. Any AI powered
healthcare app must integrate with them to be useful.
Automated updates: Patient history, lab results, and treatment notes are updated
in real time.
Workflow support: Scheduling, intake, and communication can happen directly
inside the EHR, so staff do not need to juggle multiple systems.
Accuracy: Seamless syncing reduces errors and ensures all providers are working
with the most current information.
FHIR for Interoperability
Fast Healthcare Interoperability Resources (FHIR) is the standard for health data
exchange. Without it, systems cannot communicate effectively.
Interoperability: FHIR allows different hospitals, apps, and providers to share data
securely.
Data sharing: Patient demographics, medications, lab results, and visit summaries
can be exchanged with other compliant systems.
Extensibility: FHIR makes it easier to build additional apps on top of existing
records, enhancing the functionality of an AI healthcare app like Sully AI.
Supporting Technologies
To develop an AI healthcare app like Sully AI, several other technologies must come
together:
Natural Language Processing (NLP) to interpret free-text notes and extract
structured information.
Machine Learning models that adapt over time, becoming more accurate with every
interaction.
Multilingual engines that break language barriers and make care accessible to
diverse populations.
Data security frameworks such as HIPAA-compliant encryption and access controls
to protect sensitive patient information.
These layers of technology explain why Sully AI has been successful and why the next
generation of AI healthcare apps must be built with the same depth of integration. The
combination of AI, EHR, FHIR, and supporting tools ensures that automation does not
just save time but also improves the quality and safety of care.

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Designing a User-Centric AI Healthcare Platform
A healthcare product is not remembered for the complexity of its code but for how
smoothly it fits into the lives of its users. When you set out to build an AI healthcare app
like Sully AI, design choices become just as critical as technical ones.
1. The Importance of User Experience in Healthcare
Trust in digital healthcare begins with clarity. Doctors and nurses already juggle heavy
workloads, so an interface that is simple, fast, and intuitive can make the difference
between adoption and abandonment. Patients, meanwhile, need a platform that gives
them confidence by presenting information in a way that is easy to follow and understand.
2. Aligning the Design with Real Clinical Workflows
The most successful tools are the ones that do not force providers to change their habits.
An AI healthcare app like Sully AI should reduce unnecessary clicks, mirror familiar
workflows, and integrate seamlessly into the existing environment. Supporting multiple
devices desktop for detailed records, tablets during ward rounds, and mobile apps for
patients—ensures flexibility without adding complexity.
3. Making the Experience Clear for Patients
When presented in a way they can understand, patients engage best. Multilingual
support, visual aids, and simplified instructions for medication or follow-up care make the
app more inclusive. A strong AI Consulting Company also accounts for accessibility,
ensuring people with different levels of health literacy or cultural backgrounds can use it
with ease.
4. Building Confidence Through Transparency
Doctors will not rely on a system they cannot interpret. Features such as visible
confidence scores, editable notes, and clear alerts allow users to see how the system
reached its suggestions. This transparency reassures providers that they remain in
control while using an AI powered healthcare app to support their work.
A well-thought-out design can transform a promising idea into a trusted partner for both
providers and patients. For anyone planning to develop an AI healthcare app like Sully AI,
focusing on user experience is the step that ensures long-term adoption.

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Key Features to Include in an AI Healthcare App
A strong healthcare platform should cover the entire journey from intake to follow-up.
When you set out to create an AI healthcare app like Sully AI, these features form the
foundation of a system that saves time, improves accuracy, and builds trust.
1. Real-Time Medical Scribe with Clinical Context
A digital scribe should do more than just capture words. It should listen during the
consultation, recognize symptoms, diagnoses, and treatment plans, and then generate
structured notes that fit directly into electronic health records. This reduces paperwork
and gives doctors more time with patients.
2. Smart Intake and Appointment Management
An AI-powered intake system can handle scheduling, check-ins, and digital forms before
the visit begins. By linking this feature with existing records, clinics reduce wait times and
free up front-desk staff to focus on patient care instead of repetitive tasks.
3. Automated Medical Coding and Billing Support
Correct billing codes are essential for revenue and compliance. An AI healthcare app like
Sully AI can analyze consultation notes, assign CPT and ICD codes, and even flag
missing documentation. This cuts down on errors, accelerates claims, and improves the
financial health of the practice.
4. Multilingual Communication and Translation Support
Language barriers often limit access to care. A built-in interpreter that works in real time
makes consultations smoother for patients who do not speak the local language. This
also helps clinics comply with language access requirements and deliver more equitable

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care.
5. Post-Visit Summaries and Patient Guidance
Clear follow-up instructions are as important as the consultation itself. A well-designed AI
healthcare app can automatically create patient-friendly summaries, medication
reminders, and care guidelines that are delivered through email, SMS, or in-app
notifications.
6. Virtual Nurse for Ongoing Support
Between visits, patients often have small concerns that do not require a doctor’s
attention. A virtual nurse agent can handle routine questions, provide self-care advice,
and escalate urgent cases when needed. This reduces the workload on clinical staff while
improving patient engagement.
7. Personalized AI Behavior for Each Physician
Every doctor has unique habits and preferences when it comes to documenting notes or
communicating with patients. Allowing the system to adapt to these preferences makes it
feel more natural and ensures stronger adoption.
8. Seamless Integration with EHR and EMR Systems
A modern AI powered healthcare app must integrate with major record systems like Epic,
Cerner, or Athenahealth. Supporting FHIR and HL7 standards ensures that data flows
smoothly without forcing providers to change their existing workflows.
9. Built-In Security and Compliance Features
Healthcare data is highly sensitive. End-to-end encryption, HIPAA-compliant frameworks,
and strong access controls are essential to protect patient privacy. An app that makes
security visible from the start inspires confidence among both providers and patients.
Training the AI: Clinical Data, Accuracy, and Validation
For anyone planning to develop an AI healthcare app like Sully AI, the technology is only
as good as the data and methods used to train it. A reliable healthcare assistant must be
built on medical accuracy, ethical standards, and continuous validation. Here is how the
process typically works:
1. Using the Right Clinical Data
An AI healthcare app cannot function well if it is trained only on general-purpose datasets.
It requires medical-grade information such as de-identified transcripts, structured SOAP
notes, and clinical workflows. Synthetic datasets can also be used to mimic real clinical
interactions while protecting patient identity. The goal is to make the model context-aware
so it understands how doctors and nurses actually communicate.

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2. Adapting to Physician Workflows
Doctors have different documentation styles based on their specialty. For example, a
cardiologist may focus on heart-related terminology, while a pediatrician uses child-
specific phrasing. Training the AI to mirror these behaviors makes the system feel natural.
When you build an AI healthcare app like Sully AI, personalization at this level helps with
adoption and long-term trust.
3. Ensuring Accuracy and Clinical Safety
Medical errors can have serious consequences. That is why every output must be
validated against peer-reviewed datasets, medical ontologies such as ICD and SNOMED,
and structured reference materials. Benchmark testing and physician feedback loops are
used to keep the model safe for real-world use. An effective AI powered healthcare app
never relies on unchecked outputs but works under human oversight.
4. Ethical Data Use and Governance
Respect for patient privacy is central. Training pipelines should rely only on data with
approved usage rights and include strict bias checks to avoid unequal outcomes across
demographics. Audit trails, HIPAA compliance, and GDPR alignment ensure that the
system is both transparent and accountable. These measures protect both patients and
healthcare providers who depend on the technology.
By following these steps, developers can create an AI healthcare app like Sully AI that is
not only intelligent but also clinically reliable and ethically sound. Accuracy and trust are
what separate healthcare-grade applications from general AI products.
Development Steps for Building an AI Healthcare App
Here is a practical, phased roadmap you can use to create an AI healthcare app like
Sully AI. Each phase lists goals, core tasks, owners, and clear success signals so your
team can execute with confidence.
Phase 1. Define the care journey and the AI agent roles
Goal Map the full patient journey and decide where automation adds measurable value.
Core tasks
• Run discovery with clinicians, nurses, admins, and billing teams
• Identify friction points across intake, visit, documentation, coding, follow up
• Select the first set of ai agents into healthcare apps such as scribe, intake, coding,
virtual nurse, receptionist
Owners Product lead, clinical advisors, solutions architect
Success signals A service blueprint with agent responsibilities, data inputs and outputs,
and target outcomes such as minutes saved per visit

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Phase 2. Prioritize use cases with clinical and business impact
Goal Pick high value workflows for the first release.
Core tasks
• Score use cases by time saved, revenue impact, risk, and build effort
• Define acceptance criteria per use case
• Lock the MVP scope for your AI healthcare app
Owners Product lead, clinical lead, finance partner
Success signals A signed MVP scope that can be shipped in one quarter with clear KPIs
Phase 3. Choose foundation models and training approach
Goal Select the model strategy that balances accuracy, latency, cost, and privacy.
Core tasks • Evaluate hosted models and fine tuning options
• Plan domain adaptation using de identified transcripts, SOAP notes, and synthetic
clinical data
• Set up red teaming and safety tests for medical contexts
Owners ML lead, privacy lead, MLOps engineer
Success signals
Model card with benchmarks for scribing accuracy, coding precision, and latency targets
for an ai powered healthcare app
Phase 4. Design agent based system architecture
Goal Create a modular backend where each agent is a service with clear contracts.
Core tasks • Define orchestration, event bus, and retries
• Standardize prompts and tools per agent
• Plan guardrails, human in the loop, and fallbacks
Owners Principal engineer, platform architect
Success signals
Architecture diagram, interface specs, and test stubs for every agent needed to Build an
AI Healthcare App like Sully AI
Phase 5. Build real time voice and clinical language understanding
Goal Capture ambient speech, transcribe reliably, and structure clinical data.
Core tasks • Implement streaming ASR tuned for clinical speech
• Extract symptoms, assessments, and plans into structured fields
• Generate EHR ready notes in the clinician’s preferred style

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Owners Speech engineer, NLP engineer, UX writer
Success signals Word error rate within target, note acceptance rate by clinicians above
target, end to end latency within limits
Phase 6. Integrate with EHR using FHIR and HL7
Goal Read and write to records without breaking existing workflows.
Core tasks • Connect to Epic, Cerner, Athenahealth or the chosen EHR
• Use FHIR resources for patients, encounters, observations, medications
• Sync scheduling, notes, orders, and codes with audit trails
Owners Integration engineer, compliance lead
Success signals
Bi directional sync in a sandbox, round trip tests passing, audit logs verified for AI
Healthcare App Development
Phase 7. Ship core features for the MVP
GoalDeliver value quickly while staying safe and compliant.
Core tasks • Real time scribe with clinician review and one click accept
• Smart intake and receptionist for forms, check ins, reminders
• Automated medical coding assistant that flags missing documentation
• Patient friendly summaries and follow up guidance
• Role based access, encryption, and full audit logs
OwnersFeature squads with QA and clinical reviewers
Success signals Clinician satisfaction above target, reduction in minutes per note,
coding accuracy uplift, successful pilot in one site
Phase 8. Hardening for privacy, security, and compliance
Goal Operate within HIPAA and related standards from day one.

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Core tasks • End to end encryption, secrets management, key rotation
• Role based access control and least privilege
• Data retention policies and deletion workflows
• External security audit and penetration testing
• Document controls for HIPAA and mapping toward HITRUST or ISO 27001
Owners Security lead, compliance officer, DevOps
Success signals Audit passed, findings remediated, policies approved, customer data
not used for model training unless contractually allowed
Phase 9. Pilot in a clinical setting and iterate
Goal Prove value with real users before broad rollout.
Core tasks • Select a friendly clinic and champion physicians
• Train staff and set help channels for quick support
• Track adoption, note acceptance, coding accuracy, and throughput
• Run weekly feedback loops and ship rapid fixes
Owners Customer success, product, engineering on call
Success signals
Time saved per clinician per day, higher visit throughput, fewer documentation errors,
positive user feedback for your AI healthcare app like Sully AI
Phase 10. Scale, personalize, and expand the agent team
Goal Move from a good pilot to a reliable product.
Core tasks • Personalize note styles and shortcuts per physician
• Add multilingual support and accessibility features
• Expand agents to research support and care navigation
• Improve cost per visit using caching, batch jobs, and autoscaling
• Build admin analytics for leaders and compliance teams
Owners Platform team, ML team, analytics team
Success signals
Stable performance across sites, cost within budget, growing usage, strong renewal
intent for customers who Develop an AI Healthcare App like Sully AI
Delivery checklist to keep the program on track
• Product requirements with success metrics per use case
• Threat model and privacy impact assessment
• Model card with clinical safety notes and known limits
• EHR integration runbook and rollback plan

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• Validation protocol with clinician sign off
• Support playbook and on call rotation
• Post launch roadmap for the next two quarters
Suggested timeline for a first release
• Month one to two: Discovery, journey mapping, data governance, model selection
• Month three to four: Architecture, EHR sandbox integration, voice and NLU prototype
• Month five to six: MVP features, security hardening, internal acceptance testing
• Month seven: Pilot launch with two to three providers and weekly iteration
• Month eight and beyond: Multi site rollout, personalization, cost optimization, new
agents
Metrics that prove product value
• Minutes saved per note and per visit
• Note acceptance rate without edits
• Coding precision and number of denied claims
• Patient no show reduction due to better reminders
• Visits per provider per day
• Cost per successful visit for the ai powered healthcare app
This roadmap is built to help you create an AI Healthcare App like Sully AI that ships fast,
stays safe, and scales. It balances real clinical needs with technical depth so your team
can move from concept to a product that clinicians actually use.
Cost to Build an AI Healthcare App like Sully AI
The investment required to develop an AI healthcare app like Sully AI depends on factors
such as the scope of features, integrations, and compliance needs. Below is a breakdown
in US dollars to give a realistic picture of budgets.
Phase-Wise Estimate
Total one-time estimate: $65,000 – $150,000 for a focused MVP of an AI healthcare app
like Sully AI, excluding monthly operational costs.
Factors That Influence Budget
Depth of EHR integration: Simple read-only connections are cheaper, while full bi-
directional sync across modules costs more.
Voice and speech accuracy: Higher accuracy for diverse accents and noisy
environments requires extra investment.

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Compliance level: HIPAA is essential, but HITRUST or ISO 27001 certifications
add to cost.
Scope of AI agents: A basic scribe and intake agent are leaner, but adding coding,
virtual nurse, and research agents increases complexity.
Personalization features: Custom note styles per physician improve adoption but
add development work.
Sample Scenarios
Small clinic starter
One specialty, one EHR, with scribe and intake agents. Estimated budget: $70,000
– $90,000. Timeline: ~6 months to pilot.
Multi-site network
Two specialties, deeper EHR integrations, scribe + intake + coding agents.
Estimated budget: $110,000 – $150,000.
Enterprise program
Multiple sites and specialties, multilingual features, full agent suite, advanced
certifications. Budget starts at $180,000+ and is usually phased over several
quarters.
Ongoing Costs
Model and infrastructure: Inference, storage, monitoring, and vector databases.
Typically calculated as cost per successful visit.
Maintenance and updates: API changes from EHR vendors, new specialties, and
continuous UX improvements.
Clinical oversight: Regular reviews of documentation and coding accuracy with
advisory clinicians.

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How to Keep Costs Efficient?
Begin with three high-impact features (scribe, intake, coding).
Validate EHR flows in a sandbox before rolling out widely.
Reuse common data formats across AI agents.
Focus only on KPIs that prove value: minutes saved, accuracy of notes, coding
precision, and patient throughput.
With a structured approach, you can build an AI healthcare app like Sully AI at a cost that
makes sense for your organization and deliver measurable value from the very first pilot.
Challenges in Building an AI Healthcare App like Sully AI
Creating an advanced healthcare platform comes with unique hurdles. To develop an AI
healthcare app like Sully AI, teams must address these challenges early so the product
can scale safely and win provider trust.
1. Protecting Patient Data and Privacy
Problem: Healthcare apps handle extremely sensitive data. Any breach of privacy could
lead to legal penalties and loss of trust. Regulations like HIPAA and GDPR demand strict
security practices.
Solution: Build the app with end-to-end encryption, role-based access control, and
regular security audits. Use anonymized or de-identified data for training. By meeting
HIPAA standards from day one, an AI powered healthcare app can ensure safety and
compliance.
2. Ensuring Clinical Accuracy
Problem: If AI produces inaccurate notes, wrong billing codes, or misleading
suggestions, patient safety is at risk. Errors in healthcare apps are far more critical than in
consumer apps.
Solution: Train the model using verified medical datasets and test it against peer-
reviewed benchmarks. Keep a human in the loop for high-risk outputs. Continuous
monitoring and physician feedback loops ensure the AI healthcare app like Sully AI
remains reliable in real-world use.
3. Navigating Regulations and Certifications
Problem: An app used in clinics is not just software — in many cases it can be classified
as a medical device. This brings extra regulatory hurdles, including possible FDA or CE
approval.
Solution: Engage compliance experts early and document every development step. If
needed, prepare the system for FDA or CE certification. Regular internal audits and
transparent documentation strengthen the case for regulatory approval.

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4. Integrating with Complex EHR Systems
Problem: Hospitals often run on large, complex EHR systems. Poor integration can slow
adoption or even stop providers from using the app altogether.
Solution: Build with FHIR and HL7 standards to ensure interoperability. Offer APIs and
connectors for major vendors such as Epic, Cerner, and Athenahealth. Smooth
integration makes the app feel like a natural extension of the existing workflow.
5. Overcoming Clinician Adoption Barriers
Problem: Doctors are already under pressure, and many are hesitant to learn new tools.
If the system feels clunky or adds extra steps, adoption will fail.
Solution: Focus on simple, intuitive design and allow physicians to personalize how the
app behaves. Features such as editable notes, transparent AI suggestions, and real-time
assistance help build trust and ease adoption of AI healthcare apps.
By preparing for these challenges, organizations can build an AI healthcare app like Sully
AI that is not only compliant and accurate but also trusted by clinicians and patients alike.
Conclusion
The future of healthcare is inseparable from artificial intelligence. From real-time scribing
to automated coding and multilingual patient support, platforms like Sully AI show how
technology can take away the burden of administration while giving back time to
clinicians. For healthcare organizations, the opportunity to create an AI healthcare app
like Sully AI lies not just in following a trend but in solving pressing challenges such as
burnout, inefficiency, and rising costs.
The global shift toward AI healthcare apps is already underway, with billions of dollars in
investment flowing into this space. Providers are searching for solutions that are secure,
accurate, and easy to adopt. By focusing on usability, compliance, and personalization,
you can build an app that delivers value from the very first pilot and scales confidently
across clinics and hospitals.
Why Choose Ment Tech Labs for AI Healthcare App development?
At Ment Tech Labs, we specialize in building intelligent healthcare platforms that combine
advanced technology with practical usability. Our team has hands-on experience creating
solutions similar to sully.ai – AI medical assistant, with deep expertise in clinical
workflows, AI engineering, and enterprise-grade compliance.

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What sets us apart?
Expertise in healthcare AI: We know all about EHR integration, HIPAA
compliance, and clinical accuracy, making us a reliable partner for AI healthcare app
development.
Tailored solutions: Instead of one-size-fits-all products, we design systems that
adapt to your specialties, workflows, and patient needs.
Proven results: We have delivered platforms for providers across multiple
healthcare domains, helping them reduce documentation time, improve coding
accuracy, and enhance patient engagement.
Security-first approach: Every build includes encryption, privacy controls, and
compliance audits so your AI powered healthcare app is safe from day one.
Scalable architecture: Our modular designs let you start small and expand,
whether you need a scribe, a coding agent, or a full suite of AI-driven healthcare
assistants.
Choosing Ment Tech Labs means working with a partner that not only helps you develop
an AI healthcare app like Sully AI but also ensures your product is secure, compliant, and
ready to grow with your organization.
FAQs
1. How much does Sully AI cost?
Sully AI pricing starts at around $79 per provider per month, with a premium plan at $99.
Enterprise-level organizations can request custom pricing. This flexible model makes it
accessible for both small clinics and large hospital networks.
2. What makes an AI healthcare app like Sully AI successful?

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The success of an AI healthcare app like Sully AI comes from combining clinical accuracy
with simple design. Features such as real-time scribing, billing automation, multilingual
support, and EHR integration make the app valuable for both providers and patients.
3. Can I build my own AI healthcare app similar to Sully AI?
The success of an AI healthcare app like Sully AI comes from combining clinical accuracy
with simple design. Features such as real-time scribing, billing automation, multilingual
support, and EHR integration make the app valuable for both providers and patients.
4. How do AI healthcare apps integrate with EHR systems?
Most modern AI powered healthcare apps integrate with leading systems such as Epic or
Cerner through FHIR and HL7 standards. This ensures patient records are updated in
real time and clinicians can work without switching between multiple tools.
5. Why should I invest in building an AI healthcare app?
Investing in AI healthcare apps helps providers save hours of administrative work every
day, improve accuracy in billing, and offer better patient engagement. The market for
healthcare AI is growing rapidly, making it a strong long-term opportunity for clinics,
startups, and enterprises.