AI-Powered Chatbots for Healthcare

Get a Truly Smart Medical Chatbot

Leveraging 34 years in AI technology, ScienceSoft develops medical chatbot products and custom solutions with cutting-edge functionality for healthcare providers.

AI-Powered Chatbots for Healthcare - ScienceSoft
AI-Powered Chatbots for Healthcare - ScienceSoft

Medical Chatbots with AI in Brief

Perfectly imitating human interaction, AI-powered medical chatbots can improve the quality and availability of care and patient engagement, drive healthcare and administrative staff productivity, facilitate disease self-management. AI chatbots often complement patient-centered medical software (e.g., telemedicine apps, patient portals) or solutions for physicians and nurses (e.g., EHR, hospital apps).

Essential Market Insights

The global healthcare chatbot market was estimated at $184.6 million in 2021. By 2028, it is forecasted to reach $431.47 million, growing at a CAGR of 15.20%. The rise in demand is supported by increased adoption of innovations, lack of patient engagement, and need to automate initial patient assessment.

Medical Chatbots, Explained


The natural language processing module recognizes the essence of a person’s audio or text message (symptoms description, etc.) and transforms it into a structured request. Then, AI chatbot can:

  1. Trigger the data retrieval (e.g., potential diagnoses, patient health records) from a knowledge base or an integrated app (e.g., EHR, CRM, HealthKit, Google Health).
  2. Authorize the requested operation in the integrated app (e.g., schedule an appointment).
  3. Turn to the recommendation engine to run ML algorithms (e.g., for personalized treatment adjustments).

To develop an AI-powered healthcare chatbot, ScienceSoft's software architects usually use the following core architecture and adjust it to the specifics of each project.

Architecture of Medical Chatbots for Healthcare- ScienceSoft

ScienceSoft's tip: At the early stages of the project, you need to make sure that the chatbot can be easily integrated with the necessary systems, for instance, HIS, EHR, practice management system, RCM. It will facilitate activities like appointment scheduling, treatment validation, etc.

Use cases

According to Business Insider Intelligence, up to 73% of administrative tasks (e.g., pre-visit data collection) could be automated with AI. With the recent tech advancements, AI-based solutions proved to be effective for also for disease management and diagnostics. ScienceSoft's healthcare IT experts narrowed the list down to 5 prevalent use cases.

Symptoms checking

A chatbot checks patients' symptoms to identify if medical help is required. It also can connect a patient with a physician for a consultation and help medical staff monitor patients' state.

Value: Improved access to medical care, less misinformation.

Successful example: a virtual assistant using speech, text, images, and video for patient assessment.


Patient support in post-operative care and chronic disease management

A chatbot guides patients through recovery and helps them overcome the challenges of chronic diseases.

Value: 24/7 access to care support, handling non-standard questions due to the access to personal care plans and treatment protocols.

Successful example: a chatbot app for oncology patients.

Virtual assistants for medical staff

A chatbot can be a part of a doctor/nurse app helping the staff with treatment planning, adding patient records, calculating medication dosage, verifying prescribed drugs, and retrieving all the necessary patient information fast.

Value: Increased staff efficiency, improved treatment accuracy.

Therapy delivery

Often used for mental health and neurology, therapy chatbots offer support in treating disease symptoms (e.g., alleviating Tourette tics, coping with anxiety, dementia).

Value: Better access to care, addressing the shortage of medical professionals, overcoming social stigma.

Successful example: a chatbot offering cognitive-behavioral therapy.

General patient assistance

AI chatbots provide basic informational support to patients (e.g., offers information on visiting hours, address) and performs simple tasks like appointment scheduling, handling of prescription renewal requests.

Value: 24/7 assistance availability; decreased load on the call center; patient convenience.


Patient survey before/after the appointment

A friendly AI chatbot that helps collect necessary patient data (e.g., vitals, medical images, symptoms, allergies, chronic diseases) and post-visit feedback.

Value: routine tasks automation.

Senior Business Analyst and Healthcare IT Consultant at ScienceSoft

At ScienceSoft, we know that many healthcare providers doubt the reliability of medical chatbots when it comes to high-risk actions (therapy delivery, medication prescription, etc.). To avoid costly mistakes, we often opt for iterative development. With each iteration, the chatbot gets trained more thoroughly and receives more autonomy in its actions. For example, on the first stage, the chatbot only collects data (e.g., a prescription renewal request). After more training, the chatbot's scope of actions is expanded.


With 100+ successful projects for healthcare, ScienceSoft shares AI chatbot functionality that has been in demand recently.

Speech recognition

Helps simplify the work of medical professionals and access to care for patients. Speech recognition functionality can be used to plan/adjust treatment, list symptoms, request information, etc.

Analytics of patient records and health data

Backed by sophisticated data analytics, AI chatbots can become a SaMD tool for treatment planning and disease management. A chatbot can help physicians ensure the medications' compatibility, plan the dosage, consider medication alternatives, suggest care adjustments, etc.

Human-like response generation

Using AI to imitate an actual conversation, medical chatbots will send personalized messages to users.

Patient monitoring

When aimed at disease management, AI chatbots can help monitor and assess symptoms and vitals (e.g., if connected to a wearable medical device or a smartwatch).

Notifications for patients and medical staff

A chatbot can send reminders like taking medication or measuring vitals to patients. In case of an emergency, a chatbot can send an alert to a doctor via an integrated physician app or EHR.

Patient data collection

To accelerate care delivery, a chatbot can collect required patient data (e.g., address, symptoms, insurance details) and keep this information in EHR.

Appointment scheduling

A chatbot helps select a doctor and choose a suitable date and time slot. After confirmation, the visit is scheduled in EHR.

Prescription refilling or renewal

Patients can request prescription refilling/renewal via a medical chatbot and receive electronic prescriptions (when verified by a physician).

Information sharing

Using the integrated databases and applications, a chatbot can answer patients’ questions on a healthcare organization’s schedule, health coverage, insurance claims statuses, etc.

How ScienceSoft Puts AI Chatbot Technology Into Practice

Development of a Patient Mobile App with an Integrated Medical Chatbot

Development of a Patient Mobile App with an Integrated Medical Chatbot

A US-based care solutions provider got a patient mobile app integrated with a medical chatbot. The chatbot offered informational support, appointment scheduling, patient information collection, and assisted in the prescription refilling/renewal.

Reimagine Healthcare with AI Chatbots

Having 18 years of experience in healthcare IT, ScienceSoft can start your AI chatbot project within a week, plan the chatbot and develop its first version within 2-4 months.

ScienceSoft's software engineers and data scientists prioritize the reliability and safety of medical chatbots and use the following technologies.

Front-end programming languages




21 years





ScienceSoft uses JavaScript’s versatile ecosystem of frameworks to create dynamic and interactive user experience in web and mobile apps.

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JavaScript frameworks

Angular JS


13 years



ScienceSoft leverages code reusability Angular is notable for to create large-scale apps. We chose Angular for a banking app with 3M+ users.

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React JS



ScienceSoft achieves 20–50% faster React development and 50–90% fewer front-end performance issues due to smart implementation of reusable components and strict adherence to coding best practices.

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ScienceSoft uses Meteor for rapid full-stack development of web, mobile and desktop apps.


By using a lightweight Vue framework, ScienceSoft creates high-performant apps with real-time rendering.


With Next.js, ScienceSoft creates SEO-friendly apps and achieves the fastest performance for apps with decoupled architecture.


When working with Ember.js, ScienceSoft creates reusable components to speed up development and avoid code redundancy.

Back-end programming languages



10 years





ScienceSoft's Python developers and data scientists excel at building general-purpose Python apps, big data and IoT platforms, AI and ML-based apps, and BI solutions.

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34 years



ScienceSoft's C++ developers created the desktop version of Viber and an award-winning imaging application for a global leader in image processing.

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25 years





ScienceSoft's Java developers build secure, resilient and efficient cloud-native and cloud-only software of any complexity and successfully modernize legacy software solutions.

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Microsoft .NET


19 years





Our .NET developers can build sustainable and high-performing apps up to 2x faster due to outstanding .NET proficiency and high productivity.

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10 years



ScienceSoft delivers cloud-native, real-time web and mobile apps, web servers, and custom APIs ~1.5–2x faster than other software developers.

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16 years





ScienceSoft's PHP developers helped to build Viber. Their recent projects: an IoT fleet management solution used by 2,000+ corporate clients and an award-winning remote patient monitoring solution.

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4 years

ScienceSoft's developers use Go to build robust cloud-native, microservices-based applications that leverage advanced techs — IoT, big data, AI, ML, blockchain.

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16 years





ScienceSoft’s achieves 20–50% cost reduction for iOS projects due to excellent self-management and Agile skills of the team. The quality is never compromised — our iOS apps are highly rated.

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14 years





There are award-winning Android apps in ScienceSoft’s portfolio. Among the most prominent projects is the 5-year-long development of Viber, a messaging and VoIP app for 1.8B users.

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11 years





ScienceSoft cuts the cost of mobile projects twice by building functional and user-friendly cross-platform apps with Xamarin.

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Apache Cordova

ScienceSoft uses Cordova to create cross-platform apps and avoid high project costs that may come with native mobile development.

Progressive Web Apps

ScienceSoft takes the best from native mobile and web apps and creates the ultimate user experience in PWA.

React Native


8 years



ScienceSoft reduces up to 50% of project costs and time by creating cross-platform apps that run smoothly on web, Android and iOS.

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ScienceSoft will save you from double or even triple expenses associated with platform-specific coding by creating cross-platform apps in Flutter.


With Ionic, ScienceSoft creates a single app codebase for web and mobile platforms and thus expands the audience of created apps to billions of users at the best cost.

Machine learning platforms and services



Machine learning frameworks and libraries



Bot platforms

Databases / data storages


Microsoft SQL Server

Our Microsoft SQL Server-based projects include a BI solution for 200 healthcare centers, the world’s largest PLM software, and an automated underwriting system for the global commercial insurance carrier.


We’ve implemented MySQL for Viber, an instant messenger with 1B+ users, and an award-winning remote patient monitoring software.


ScienceSoft's team has implemented Oracle for software products used by GSK and AstraZeneca. We’ve also delivered Oracle-based SCM platform for Auchan, a retail chain with 1,700 stores.


ScienceSoft has used PostgreSQL in an IoT fleet management solution that supports 2,000+ customers with 26,500+ IoT devices. We’ve also helped a fintech startup promptly launch a top-flight BNPL product based on PostgreSQL.


Apache Cassandra

Our Apache Cassandra consultants helped a leading Internet of Vehicles company enhance their big data solution that analyzes IoT data from 600,000 vehicles.

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Apache Hive

ScienceSoft has helped one of the top market research companies migrate its big data solution for advertising channel analysis to Apache Hive. Together with other improvements, this led to 100x faster data processing.

Apache HBase

We use HBase if your database should scale to billions of rows and millions of columns while maintaining constant write and read performance.

Apache NiFi

With ScienceSoft’s managed IT support for Apache NiFi, an American biotechnology corporation got 10x faster big data processing, and its software stability increased from 50% to 99%.


ScienceSoft used MongoDB-based warehouse for an IoT solution that processed 30K+ events/per second from 1M devices. We’ve also delivered MongoDB-based operations management software for a pharma manufacturer.

Cloud databases, warehouses and storage


Amazon Redshift

We use Amazon Redshift to build cost-effective data warehouses that easily handle complex queries and large amounts of data.

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Amazon DynamoDB

We use Amazon DynamoDB as a NoSQL database service for solutions that require low latency, high scalability and always available data.

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Azure Cosmos DB

We leverage Azure Cosmos DB to implement a multi-model, globally distributed, elastic NoSQL database on the cloud. Our team used Cosmos DB in a connected car solution for one of the world’s technology leaders.

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Azure SQL Database

Azure SQL Database is great for handling large volumes of data and varying database traffic: it easily scales up and down without any downtime or disruption to the applications. It also offers automatic backups and point-in-time recoveries to protect databases from accidental corruption or deletion.

Google Cloud Platform

Google Cloud Datastore

We use Google Cloud Datastore to set up a highly scalable and cost-effective solution for storing and managing NoSQL data structures. This database can be easily integrated with other Google Cloud services (BigQuery, Kubernetes, and many more).

The Challenges Ahead and Ways to Solve Them

Taking the lead in AI projects since 1989, ScienceSoft's experienced teams identified challenges when developing medical chatbots and worked out the ways to resolve them. Check our recommendations to stay on the safe side.

#1 The accuracy of AI chatbots may affect patient outcomes.


Solution: To avoid possible chatbot malfunctioning, ScienceSoft follows the risk-based approach. We divide all AI chatbot actions into low-risk and high-risk groups. For high-risk tasks (e.g., medication prescription), we recommend allocating additional resources to AI training or adding physician verification.

#2 Medical chatbots access and handle huge data loads, making them a target for security threats.


Solution: ScienceSoft's engineers recommend employing measures like data anonymization, encryption, and user authentication mechanisms early on. Besides, throughout the project, we perform regular vulnerability assessments and penetration testing of software and the development infrastructure.

#3 Users may be annoyed by the slow response from a medical chatbot.


Solution: It may occur if the chatbot searches through an extensive data set or is overloaded with requests. At ScienceSoft, we address this concern at the architecture design stage and plan a cloud-based architecture that will handle the anticipated amount of data and temporary request spikes.

Pricing Information

The cost range depends on the functionality in the first place. The investments start from $70,000 for a relatively simple solution (e.g., providing information support, online scheduling) to $250,000 for a complex AI chatbot (e.g., with advanced clinical decision support functionality).

Wondering how much it will cost in your specific case?

Get a quote

Major cost factors of AI chatbots in healthcare

Development costs

  • Intended use and functionality of a chatbot.
  • The number of data sources to process, data quality.
  • ML algorithms' complexity and expected accuracy.
  • Compliance-associated costs (e.g., implementation of HIPAA security measures, FDA registration for healthcare chatbots with SaMD functionality).
  • UI requirements.
  • Required integrations (e.g., with EHR, third-party telehealth solutions, scheduling modules).

Operational costs

  • AI maintenance costs.
  • Infrastructure costs.

Ready to Implement a Medical AI Chatbot?

Relying on 34 years of experience in data science and AI and 18 years in healthcare, ScienceSoft develops reliable AI chatbots for patients and medical staff.

AI medical chatbot consulting

Let's play it safe! With a team of meticulous healthcare consultants on board, ScienceSoft will design a medical chatbot to drive maximum value and minimize risks.

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Development of a medical chatbot with AI

Stay ahead of the curve with an intelligent AI chatbot for patients or medical staff. Launch MVP in 2-4 months and start getting benefits fast.

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About ScienceSoft

ScienceSoft is an international software consulting and development company headquartered in McKinney, Texas. With vast experience in medical IT since 2005, we help software product companies and healthcare providers leverage chatbots in their medical software development projects to enhance the patient experience, deliver verified information fast, and automate routine processes.