Conversational AI in Healthcare: Use Cases, Examples, and Impact in 2024

What is conversational AI technology in healthcare?

Conversational AI for healthcare is a type of artificial intelligence that interacts with patients, caregivers, and healthcare staff in a natural, human-like way — through text or voice. Unlike simple chatbots that stick to scripted responses, conversational AI can understand context, respond in real-time, and adjust based on the flow of a conversation. This makes it much more intuitive, allowing patients to do things like check symptoms, book appointments, get answers to health questions, or even manage ongoing health conditions with ease.

As medical professionals increasingly combine online and in-person services, conversational AI is becoming a key part of their offering. It provides patients with around-the-clock support, making it easier to get help at any time without the wait. For healthcare teams, it takes care of routine inquiries and tasks, freeing up time for more critical work. Conversational AI is helping make healthcare more accessible and user-friendly, giving patients a way to feel supported whether they’re seeking help from home or visiting a clinic in person.

To see how this looks in practice, we’ll present you with some examples of conversational AI solutions already making an impact in healthcare.

5 real-life examples of conversational AI products tailored for healthcare

Here are five real-life solutions helping organizations enhance patient care, streamline workflows, and reduce administrative loads:

1. Amina by Black Doctor 24/7

Black Doctor 24/7 is a telemedicine platform designed by Riseapps, specifically to serve underserved communities with quick, secure access to healthcare professionals. It includes a conversational AI feature that helps with patient inquiries, appointment scheduling, and real-time symptom checking.

Key challenges addressed:

• Providing accessible, around-the-clock support for patients in underserved communities
• Offering quick access to healthcare advice and screenings, reducing patient wait times
• Ensuring HIPAA compliance while handling sensitive patient data

2. Youper

Youper is a mental health AI assistant that engages users in conversational therapy and emotional check-ins. It uses cognitive-behavioral techniques to help users manage stress, anxiety, and mood. Youper’s AI-driven insights support users in tracking their mental health progress over time.

Key challenges addressed:

• Increasing accessibility to mental health support, especially outside traditional therapy hours
• Providing immediate support for emotional distress, without needing a human therapist
• Helping users track emotional health trends to better understand and manage their well-being

(image source: Youper website)

3. K Health

K Health offers a conversational AI platform for personalized health advice and symptom checking. By comparing user symptoms with data from millions of medical records, K Health provides users with relevant insights and next steps for managing their health.

Key challenges addressed:

• Delivering affordable, on-demand medical guidance based on real-world health data
• Reducing unnecessary doctor visits by offering symptom checks and advice remotely
• Ensuring privacy and data security while using large datasets for health insights

(image source: K Health website)

4. EmpowerHealth

EmpowerHealth uses conversational AI to assist with chronic disease management and patient engagement. Its virtual assistant tracks patient symptoms, provides medication reminders, and supports daily health routines to improve adherence and overall patient health.

Key challenges addressed:

• Supporting chronic disease patients with day-to-day symptom monitoring and medication adherence
• Reducing hospital readmissions by actively engaging patients in their own care
• Providing personalized health management outside clinical settings

(image source: EmpowerHealth website)

5. Ada Health

Ada Health’s AI-driven platform assists with symptom assessments and preliminary health advice. Through an interactive conversational interface, Ada asks users about their symptoms and provides tailored health recommendations and potential next steps.

Key challenges addressed:

• Helping users understand and interpret symptoms for timely health decisions
• Reducing pressure on healthcare systems by empowering patients to self-assess
• Providing symptom assessments that can be shared with healthcare providers for follow-up

(image source: Ada Health website)

Each of these solutions shows how much conversational AI can tackle real challenges in healthcare, whether it’s supporting mental health, managing chronic conditions, or making health guidance more accessible.

Сonversational AI solutions vs. chatbots: 3 core differences

Conversational AI and regular chatbots each play valuable but distinctly different roles, shaped by their level of complexity, capabilities, and overall functionality. While both can interact with users, the experience they offer varies widely.

Regular chatbots typically stick to straightforward, rule-based responses, making them ideal for handling simple tasks. However, conversational AI goes much further, using advanced understanding to bring a more personalized, context-aware approach to patient care and support.

Conversational AI’s ability to understand intent, learn from interactions, and adapt its responses adds a layer of engagement that regular chatbots simply can’t provide. This makes it a natural fit for the nuanced world of healthcare, where understanding and responding to individual patient needs is essential.

Key differences entail:

1. Advanced understanding and contextual conversations

Conversational AI is designed to engage in dynamic, context-aware interactions by using natural language processing (NLP) and machine learning (ML). These technologies allow conversational AI solutions to understand not only what a user is saying but also the intent behind their question, enabling it to respond in a more meaningful and human-like way.

For instance, in a healthcare setting, a conversational AI could guide a patient through a symptom check, adjusting its questions based on previous responses to offer a more tailored experience.

Regular chatbots, however, operate with pre-set rules and limited responses, making them less capable of handling complex or nuanced inquiries. They often work well for simple tasks but fall short when it comes to understanding deeper layers of conversation or medical context.

2. Key differences in capabilities

Conversational AI and regular chatbots differ significantly in what they can accomplish:

Complex interactions

Conversational AI for healthcare can handle multi-step interactions, managing follow-up questions and adjusting responses based on user input. Regular chatbots are generally limited to single, straightforward questions and responses.

Learning and adaptability

Unlike regular chatbots, which stick to their initial programming, conversational AI learns from past interactions. This means it can improve its responses over time, becoming better at understanding and assisting users. For this, a generative AI development company, like Riseapps can use generative AI models or other ML techniques.

Personalized conversations

Conversational AI offers a more personalized experience, often remembering details from past interactions and responding in context. This helps create a more engaging, user-centered experience that can adapt to individual needs, whereas regular chatbots can only offer generic, one-size-fits-all responses.

3. Application in healthcare

In healthcare, these differences in capability and understanding determine how conversational AI and regular chatbots are used.

Conversational AI for healthcare is well-suited for complex tasks such as:

• patient triage
• symptom monitoring
• ongoing patient support

With its ability to interpret medical language and provide customized responses, it can guide patients through their healthcare journey in a more informed and interactive way and increase patient engagement.

Regular chatbots are best for straightforward administrative functions like scheduling appointments, sending reminders, or answering common questions about office hours and services. While helpful for streamlining routine tasks, regular chatbots lack the depth needed for more sensitive, medically-oriented interactions.

5 most popular use cases of conversational AI for healthcare

Conversational AI is making its mark in healthcare, offering patients easier ways to get support and freeing up healthcare providers from routine tasks. These AI systems aren’t just answering questions — they’re checking in on patients with chronic illnesses, guiding people through symptom assessments, and reminding them to take medications. This kind of AI is helping healthcare teams provide more consistent, accessible care without stretching their resources thin, and this way enhance patient engagement.

Below, we’ll look at specific ways conversational AI is already making a difference:

1. AI chatbots for patient support and triage

Conversational AI chatbots are increasingly handling routine patient inquiries, managing appointment scheduling, and even supporting triage by guiding patients through a few initial questions to assess the severity of their condition. With the help of insight from AI strategy consulting, these AI-powered chatbots can help direct patients to the appropriate level of healthcare delivery, reducing unnecessary ER visits and freeing up healthcare staff to focus on more critical cases.

Example: Many healthcare organizations are now using chatbots to answer basic questions and provide initial triage support, helping patients find the right care without overloading emergency rooms, and potentially improve patient outcomes.

Key features of AI chatbots for patient support:

• Natural language understanding to interpret and respond to patient questions accurately.
• Triage tools to assess the urgency of a patient’s issue based on their input.
• Automated appointment scheduling and reminder capabilities to ensure patients don’t miss follow-up care.
• Integration with telemedicine platforms for seamless referrals to virtual consultations.

2. AI virtual health assistants

AI-driven virtual health assistants are helping patients manage their health more proactively by offering 24/7 support, answering questions, and providing reminders for medications or appointments. These virtual assistants are particularly helpful for chronic care patients, as they help maintain regular communication without requiring human intervention for every interaction.

Example: Virtual health assistants that monitor patient health in real-time and provide tailored care recommendations, tracking medication adherence for chronic patients.

Key features of conversational AI virtual health assistants:

• Real-time health monitoring to track patient vitals or other health indicators and send alerts when necessary.
• Automated check-ins with patients to monitor symptoms over time.
• Secure data exchange with EHRs to ensure updated, accessible health information for providers.
• Natural language processing for smooth, human-like interactions that feel supportive.

3. Conversational AI for chronic disease management

For patients with chronic conditions like diabetes, conversational AI can provide ongoing support, helping them stay on top of treatment plans and monitoring symptoms to detect potential problems early. This approach reduces the risk of complications and helps keep hospital readmissions to a minimum.

Example: AI-powered chatbots that check in daily with chronic patients, reminding them of their treatment regimen and monitoring vital signs for any abnormalities.

Key features for chronic disease management:

• Continuous symptom tracking and reporting to keep healthcare providers updated.
• Predictive alerts for doctors based on real-time patient data to catch early warning signs.
• Integration with wearable devices to seamlessly incorporate real-time data into patient records.
• Proactive health recommendations to guide patients toward better self-care practices.

4. Conversational AI for medication adherence and reminders

Medication adherence is a common challenge, especially for patients with complex prescriptions. Conversational AI provides interactive reminders, tracks adherence, and answers medication-related questions. This not only reduces missed doses but also enhances the overall effectiveness of treatment plans.

Example: Conversational AI systems that remind patients to take their medication on time, offering guidance if they have questions about side effects or dosage.

Key features for medication adherence:

• Timely reminders customized to individual schedules for improved adherence.
• Adherence tracking to monitor whether patients are following their prescriptions.
• Side effect alerts and guidance on potential drug interactions.
• Integration with patient records to provide personalized advice based on health history.

5. Conversational AI for mental health support

Mental health support is another area where conversational AI can make a big difference. These conversational AI assistants engage users in guided exercises, offer mood-tracking tools, and provide techniques for managing anxiety or stress. While they don’t replace therapists, they offer accessible, real-time support that’s invaluable between professional sessions.

Example: A conversational AI mental health assistant that checks in with users, offering stress-relief exercises and helping users track their moods over time to notice patterns.

Key features for mental health support

• Mood tracking and analysis to help users recognize emotional trends.
• Guided exercises for managing stress and anxiety, such as breathing techniques or CBT-inspired activities.
• Personalized coping strategies and reminders to encourage regular mental wellness practices.
• Guidance on seeking professional help if the user’s symptoms indicate a need for more comprehensive care.

Conversational AI in healthcare isn’t just about automating tasks — it’s about creating meaningful, supportive interactions that make patient care easier, more consistent, and more accessible.

By handling routine tasks and supporting patient needs in real-time, these conversational AI solutions are allowing healthcare providers to extend the level of care they offer while keeping operations efficient.

Enhanced healthcare: Conversational AI in improving medical operations

Conversational AI is proving to be a practical, reliable tool in healthcare, taking on key administrative tasks so that healthcare staff can focus more on direct patient care. By managing things like scheduling, medical records, billing, and inventory, these conversational AI solutions handle essential but time-consuming jobs that often lead to bottlenecks when done manually.

Here’s a closer look at how conversational AI is improving healthcare operations, making everything run a bit more smoothly and freeing up professionals to concentrate on what they do best.

Appointment scheduling and reminders

Appointment scheduling and reminders are essential for keeping patient visits on track, but they can be time-consuming for healthcare staff. Conversational AI can handle these tasks automatically, booking, rescheduling, and reminding patients of their upcoming appointments. This not only reduces the risk of no-shows but also frees up administrative staff for more critical tasks.

Example: Conversational AI bots that allow patients to easily book or reschedule appointments while automatically sending reminders, reducing the likelihood of missed visits.

Key benefits of introducing conversational AI for appointment scheduling and reminders:

• Reduces administrative workload, allowing staff to focus on more complex tasks
• Minimizes no-shows, improving the efficiency of healthcare operations
• Provides patients with a seamless experience, making it easier for them to manage their appointments

Medical record management

Managing medical records accurately is a challenge in busy healthcare settings, often leading to errors when done manually. Conversational AI can help by automating data entry, organizing patient information, and keeping records up-to-date in real time.

With these virtual assistants, healthcare providers can access and update records during consultations, helping them focus more on patient interaction and care.

Example: Virtual assistants used by doctors to record patient information during visits, ensuring that all necessary details are accurately documented without interrupting the consultation.

Key benefits of introducing conversational AI for medical record management:

• Reduces manual errors, ensuring that records are accurate and current
• Allows healthcare professionals to focus on patient care rather than data entry
• Ensures faster access to patient information, improving the flow of care

Riseapps has one such project in the portfolio.

PareIT is an AI-driven solution that transforms medical record management by summarizing complex medical documents in seconds. It cuts down the time healthcare providers spend on documentation by up to 40x. Instead of lengthy manual processing, PareIT’s tool generates concise summaries, offering a quick, clear view of patient records that saves both time and resources.

It’s designed with secure access and HIPAA compliance and speeds up documentation but also reduces costs — making record management three times more affordable than traditional methods handled by legal or clinical staff. For healthcare teams, this means less time spent on paperwork and more time focused on patient care.

Insurance verification and billing assistance

Insurance verification and billing can often be sources of frustration for both patients and healthcare staff. Conversational AI simplifies this process by verifying insurance details ahead of appointments and helping patients understand their billing statements. Patients can access information about their coverage, ask about charges, and even set up payment plans with the help of conversational AI, reducing the burden on administrative staff and improving the patient experience.

Example: Conversational AI bots that confirm coverage, clarify billing, and assist patients in setting up payment options, making the financial side of healthcare easier to navigate.

Key benefits of introducing conversational AI for insurance verification and billing assistance:

• Reduces administrative workload related to insurance and billing inquiries
• Helps patients understand and manage their healthcare costs
• Improves transparency, which leads to greater patient satisfaction and trust

Inventory and supply chain management

Keeping essential supplies available is critical in healthcare, where delays or shortages can impact patient care. Conversational AI can monitor inventory levels in real-time, automatically reorder supplies when they run low, and send alerts to staff as needed. By automating inventory management, conversational AI reduces the need for manual checks and ensures that healthcare facilities are always stocked with essential items, from medications to protective equipment.

Example: AI-driven inventory systems that track stock levels, initiate reorders, and alert staff when items are running low to ensure continuous availability.

Key benefits of introducing conversational AI for inventory and supply chain management:

• Prevents stockouts of essential supplies, reducing operational disruptions
• Frees up staff from routine inventory checks, allowing them to focus on patient care
• Improves the overall efficiency of supply chain management, ensuring resources are used effectively

With conversational AI taking on these foundational tasks, healthcare organizations can achieve smoother operations, reduce the potential for human error, and ultimately provide a better experience for both patients and staff.

Ready-made conversational AI healthcare solutions vs. custom development

When it comes to bringing conversational AI into healthcare, organizations have two main choices: they can opt for ready-made solutions or go the custom route. Each has its strengths and trade-offs, depending on what the organization needs.

Ready-made solutions are typically faster and more budget-friendly, offering straightforward tools that help with everyday tasks.

In contrast, custom-built solutions take longer to develop and require a higher investment but deliver a tailored fit that can adapt to unique workflows and complex requirements.

Here’s a closer look at what each approach offers and when each might be the right choice:

Ready-made conversational AI solutions

Ready-made conversational AI solutions are pre-designed to handle standard healthcare functions and are generally easy to implement. These solutions are often subscription-based, allowing healthcare providers to quickly access conversational AI capabilities with minimal setup. Ready-made solutions are ideal for streamlining general administrative tasks and are commonly used to manage functions like appointment scheduling, patient reminders, and basic FAQs.

Benefits

Quicker implementation

Ready-made solutions require little customization, so they can be up and running quickly, which is ideal for organizations looking to streamline operations without delay.

Lower cost

Since these solutions are pre-built, the initial investment is significantly lower than custom development, making them accessible for smaller healthcare facilities or those with budget constraints.

Reliable for standard tasks

Ready-made solutions work well for handling repetitive administrative tasks, such as booking appointments, answering common inquiries, and sending reminders, where extensive customization isn’t required.

Limitations

Limited customization

Ready-made solutions often come with set features, which may not fully align with the unique workflows or specific requirements of a healthcare provider. Customizing them to fit specialized needs is usually limited.

Lack of deep integration

Pre-built solutions may not integrate seamlessly with existing systems, such as EHR platforms, billing systems, or telemedicine services, potentially creating silos in information flow and limiting their usability.

Not ideal for complex use cases

Ready-made solutions might not provide the level of conversational depth or medical accuracy required for more involved patient interactions, such as chronic disease management or personalized patient follow-up.

Custom conversational AI solutions

Custom conversational AI solutions are designed specifically for an organization’s needs, allowing healthcare providers to shape the system according to their workflows, patient demographics, and clinical requirements.

Benefits

Tailored to specific needs

Custom solutions can be developed to support specialized workflows, such as advanced patient triage, symptom tracking, or chronic care management, making them a valuable tool for organizations with unique requirements.

Seamless integration with existing systems

Custom AI can be engineered to work with other systems like EHRs, telemedicine platforms, and patient portals, creating a unified experience and enabling comprehensive data sharing across platforms.

Scalability and flexibility

With a custom-built solution, healthcare providers can scale and adjust the system as needs evolve, adding new features or refining existing ones over time, which ensures that the investment continues to add value.

Enhanced security and compliance

Custom solutions can be designed to meet strict healthcare compliance standards, such as HIPAA and GDPR, ensuring patient data privacy and security are maintained at every stage of the interaction.

Limitations

Longer development and implementation timeline

Building a custom solution takes time, from initial design to testing and deployment, making it a slower route compared to off-the-shelf options.

Higher initial costs

Custom solutions require a larger upfront investment due to the level of development, testing, and ongoing maintenance involved, which may be challenging for smaller organizations with limited budgets.

Ongoing maintenance and support needs

Custom solutions often require dedicated support and regular updates, which adds to the long-term cost and resource commitment.

You can use AI consulting services to understand which option is best for you.

How to build a conversational AI solution for healthcare

Creating a custom conversational AI solution for healthcare is a multi-layered process that requires careful planning, the right tools, and ongoing refinement. Here’s a brief look at each critical stage involved:

Stage 1: Planning and research

Project managers and business analysts kick things off by defining the purpose of the solution, identifying specific patient needs, and ensuring compliance with regulations like HIPAA and GDPR. With a clear picture of the audience and regulatory requirements, they set a solid foundation for development.

Stage 2: Choosing the right tech stack

The tech lead selects essential tools, such as NLP, machine learning models, secure APIs, and real-time analytics, that allow the AI to interact, learn, and provide accurate responses.

Stage 3: Architecture design

Software architects design a scalable framework that includes chatbot functionality, API integration, and plans for future growth. This step is key to ensuring smooth, reliable operations.

Stage 4: Data collection

Data engineers gather a domain-specific dataset and curate a knowledge base, making sure the data complies with regulatory standards to maintain patient engagement and trust and meet legal requirements.

Stage 5: Data slicing for RAG model

Machine learning engineers segment data, optimize for real-time retrieval, and set up regular updates so that the RAG (Retrieval-Augmented Generation) model can provide up-to-date, relevant information quickly.

Stage 6: User experience design

UX/UI designers craft a patient-centered interface that focuses on accessibility, ease of use, and an emotionally intelligent design. A friendly, intuitive interface helps users feel comfortable interacting with the health system.

Stage 7: Integrating with healthcare systems

Integration engineers connect the system with EHR systems and telemedicine platforms, ensuring that patient information is secure and easily accessible. Secure data exchange protocols help maintain compliance and protect privacy.

Stage 8: Conversation design

Conversational designers develop natural flows and script responses to handle personalization and error management, creating a smoother, more human-like user experience.

Stage 9: Testing and deployment

QA testers conduct functional tests, User Acceptance Testing (UAT), and security and performance checks to confirm that everything works as it should before the conversational AI goes live. Monitoring post-launch keeps performance on track.

Stage 10: Pilot testing and soft launch

The team rolls out the AI in a controlled environment, gathering patient feedback and making adjustments based on real interactions, letting the AI learn and improve early on.

Stage 11: Patient education and onboarding

The support team provides tutorials and guides, helping patients understand how to use the AI while also setting up feedback channels to encourage positive experiences and valuable insights.

Stage 12: Ongoing training and continuous learning

Machine learning engineers retrain the model based on user feedback and system updates, keeping the AI responsive and relevant as it gains more experience.

Stage 13: Post-launch compliance and security monitoring

Compliance officers and security teams conduct regular audits for HIPAA/GDPR, update encryption as needed, and continuously monitor access to keep the AI safe and secure while adhering to regulations.

A custom conversational AI solution built with these stages in mind will not only meet healthcare needs but also deliver an engaging, patient-centered experience that supports quality care and operational efficiency.

Why Riseapps for conversational AI solution development

When it comes to implementing conversational AI in healthcare, Riseapps brings the flexibility to integrate existing solutions or create custom AI systems from the ground up.

With a track record of designing AI-powered systems specifically for healthcare, Riseapps knows how to align technology with the unique demands of the medical field. Whether it’s setting up a ready-made solution to quickly streamline operations or building a tailored AI system to support specialized workflows, Riseapps combines healthcare sector knowledge and technical expertise to deliver tools that truly fit.

For example, Riseapps created Amina, a conversational AI chatbot for Black Doctor 24/7, a telemedicine platform tailored to provide accessible healthcare for underserved African American communities.

With a conversational AI feature, the platform ensures quick, reliable assistance by answering patient inquiries, helping with appointment scheduling, and even performing initial symptom assessments.

Client request

The team behind Black Doctor 24/7 needed an efficient AI-powered solution to support their telemedicine services, prioritizing HIPAA compliance and fast, reliable responses for patient inquiries. They were looking for conversational AI that could handle frequent, straightforward questions while guiding patients through their initial symptoms before connecting them to doctors.

Challenges

Developing a conversational AI solution that could seamlessly interact with patients, recognize sensitive information, and meet HIPAA’s strict data privacy requirements was no easy feat. In addition, the AI needed to handle a large volume of inquiries in a short period, delivering accurate information without the wait time.

Riseapps’ solutions

• Riseapps integrated a conversational AI component — chatbot Amina — into Black Doctor 24/7’s system, ensuring it could understand medical terms and direct patients to appropriate resources.
• By implementing a secure, HIPAA-compliant framework, the AI safely handled patient data and offered a user-friendly experience for patients seeking health support.
• To address large volumes of users, Riseapps optimized the system for speed and reliability, so that inquiries and initial assessments were managed swiftly.

Project results

1. In just two weeks, Black Doctor 24/7 successfully screened over 30,000 patients, providing timely, accessible support to a community often underserved by traditional healthcare.
2. The conversational AI streamlined patient interactions, reduced wait times, and improved the overall experience, meeting both the platform’s goals and the patients’ needs for quick, reliable healthcare guidance.

Our experience spans across HIPAA-compliant systems, patient-facing chatbots, and AI solutions integrated directly with EHRs, telemedicine platforms, and other healthcare tools. Working with Riseapps means partnering with a team that not only understands AI but knows healthcare inside and out, ensuring that every solution meets regulatory standards while enhancing both patient care and operational efficiency.