AI chatbots for healthcare: A guide for medical centers and healthcare providers

In most industries, it’s quite simple to create and deploy a chatbot, but for healthcare and pharmacies, things can get a little tricky. You’re dealing with sensitive patient information, diagnosis, prescriptions, and medical advice, which can all be detrimental if the chatbot gets something wrong.

So, how do healthcare centers and pharmacies incorporate AI chatbots without jeopardizing patient information and care? In this blog we’ll walk you through healthcare use cases you can start implementing with an AI chatbot without risking your reputation. 

What is a healthcare chatbot? 

Healthcare chatbots, or medical chatbots, are intelligent assistants used by medical centers, pharmacies, and medical professionals to help patients get assistance faster. They can be AI-powered to handle more complex situations and provide a conversational experience to the patient, critical in a sensitive situation.  

Here are a few examples of tasks they can help with: 

• FAQs 
• Appointment booking 
• Reminders 
• Symptom tracking and triage 
• Administrative tasks 

What are the benefits of a healthcare chatbot? 

• Always-on availability: Not every healthcare provider can give patients 24/7 support, but a healthcare chatbot can help make that possible by providing round-the-clock access to medical services like appointment booking, FAQs, and much more. 
• Speed up time to resolution: Using a chatbot can help healthcare centers resolve various patient issues much faster, leaving reception open to urgent calls and complicated issues. 
• Improve operational and cost efficiency: Healthcare chatbots automate administrative tasks, reduce call center workload, send appointment reminders, and efficiently triage patients, resulting in measurable reductions in administrative work, missed appointments, and ER visits. These efficiencies also help decrease per-visit costs, freeing staff for more complex patient needs. 
• Offer anonymity: Many patients value privacy and prefer to ask sensitive medical questions anonymously. Chatbots provide a judgment-free way to seek information without revealing their identity. 
• Scalable solution: A medical chatbot is a scalable solution, meaning healthcare providers can start simple, test, adjust, and add more use cases, and expand the capabilities of the solution. It also allows medical centers to manage multiple patients at the same time, easing the workload of human workers. 

15 use cases for AI medical chatbots 

15. Handover to a medical professional

Your chatbot can schedule and set up calls with a tele-health professional. Use video or voice to transfer patients to speak directly with a healthcare professional with solutions like WhatsApp Calling, Viber Business Calls, or traditional Voice.  

An AI chatbot is also trained to understand when it can no longer assist a patient, so it can transfer patients to speak with a representative or healthcare professional and avoid any unpleasant experiences.   

Healthcare chatbot technology (AI, automation, governance, and more) 

So, what kind of technology actually enables these use cases and helps you build a reliable medical chatbot? There are several core components that typically work together. You don’t need to implement all of them from day one, but you should understand what each brings to the table. In practice, the first step is almost always speaking with an AI expert who can help you design the right mix based on your use case, risk level, and regulatory requirements. 

Here are the key technologies involved: 

1) Natural language processing (NLP)

Natural language processing (NLP) enables medical chatbots to understand, interpret, and generate human language, including medical terms, abbreviations, and informal phrases. By extracting key details such as symptoms, medications, and dates, and handling spelling mistakes, NLP allows chatbots to engage in accurate, meaningful conversations with patients by interpreting free-text questions, recognizing medical terminology, and differentiating between symptoms, conditions, and procedures for effective triage, scheduling, and improved patient interactions.

2) Machine learning (ML) and large language models (LLMs)

Machine learning drives the chatbot’s ability to detect user intent, predict next actions, personalize advice, and generate medically accurate responses. It adapts through user feedback, ensuring continual improvement. 

Key features enabled by machine learning include: 

• Identifying user goals (e.g., booking appointments, managing prescriptions) 
• Choosing appropriate chatbot actions or escalating to humans 
• Tailoring messages using patient data 

Healthcare chatbots use large language models (LLMs) and retrieval-augmented generation (RAG) to: 

• Produce human-like answers grounded in approved medical sources 
• Minimize errors by referencing clinical guidelines and protocols 
• Handle multiple languages efficiently 

Ongoing learning from interactions and surveys helps refine accuracy and performance over time. 

3) Speech recognition and voice technologies

Converts spoken words to text (ASR) and text to speech (TTS), enabling voice-based interactions for accessibility and convenience. For voice‑based healthcare chatbots (for example, hotlines or IVR systems), speech tech is essential: 

• Automatic speech recognition (ASR): Converts spoken language into text the chatbot can understand, even with background noise, accents, or medical terms. 
• Text‑to‑speech (TTS): Converts the chatbot’s responses back into natural‑sounding speech. 

This enables voice assistants for appointment scheduling, medication reminders, and check‑in calls. It helps with accessibility for patients who prefer speaking over typing or have limited mobility or vision. 

4) Sentiment analysis and emotion detection   

Sentiment analysis in healthcare chatbots identifies emotions like anxiety or distress and adjusts responses or escalates as needed, especially in emergencies. 

• It detects user emotions through language cues and adapts replies with empathy or clearer explanations. 
• Critical messages (e.g., self-harm or severe symptoms) trigger immediate alerts for human intervention.

In regulated environments, sentiment models distinguish between routine symptoms (like “My chest feels tight when I run”) and emergencies (“I have severe chest pain right now”). Effective models route non-urgent cases appropriately and escalate high-risk situations immediately. 

5) Contextual awareness and dialog management 

The chatbot keeps track of conversation history, session context, and patient details (with consent) for personalized, compliant interactions. It remembers previous messages, workflow steps, and patient-specific factors like age or medications to avoid repetition and ensure relevant responses. Dialog management guides the conversation, decides when to escalate to a human, enforces clinical rules, and maintains consistency across all channels, such as web, apps, WhatsApp, SMS, and voice. 

6) Integration with medical systems and knowledge bases 

Healthcare chatbots provide real value when securely integrated with Electronic Health Records (EHR), scheduling, billing, medication systems, and trusted medical knowledge sources. 

Chatbots should connect to: 

• EHR/EMR for patient data updates 
• Scheduling for appointments 
• CRM for outreach and reminders 
• Billing/insurance for coverage and cost info 
• Medication management for refills and reminders 

Secure APIs and access controls protect health information, while event-driven workflows can trigger messages like new lab results or missed refills. These integrations enable chatbots to deliver reliable, secure, and context-aware care for both routine and complex tasks. 

7) Omnichannel and orchestration layer   

Provide consistent experiences on web, mobile, messaging, and voice platforms with a central layer for managing context and handoffs. To ensure your medical chatbot is effective, offer multiple communication channels, such as on apps, portals, messaging (WhatsApp, SMS, Viber, RCS), and voice, so patients can choose their preferred method. The orchestration layer maintains conversation history and context across all channels, allowing agents or clinicians to access the full patient story and enable seamless transitions. 

8) Governance and lifecycle management   

Maintain safety and compliance in healthcare AI with ongoing version control, clinical reviews, safety testing, documentation, and escalation protocols. Implementing healthcare AI requires continuous management: 

• Version control & approvals: All updates to chatbot content, rules, or models should be reviewed and approved, preferably with clinical input. 
• Testing & validation: Test new logic or models through A/B testing and safety checks to ensure reliability. 
• Documentation: Keep clear records of chatbot purpose, limitations, and escalation steps to meet internal and regulatory needs.

The right technology stack will depend on your organization’s goals, whether you’re focused on simple appointment reminders, full‑scale digital triage, or chronic disease management. That’s why engaging with AI and healthcare technology experts early is critical. They can help you design an architecture that is not only intelligent and user‑friendly, but also clinically safe, compliant, and scalable. 

Which healthcare practices use AI chatbots? 

Megi Health Platform

The Megi Health platform at the Magdalena Clinic introduced a WhatsApp-based virtual assistant using Infobip chatbot building platform for chronic-care patients, especially those with hypertension. Patients can easily share blood pressure readings, report symptoms, receive reminders, access educational materials, and connect with doctors securely. This led to a 65% reduction in data collection time and an 86% patient satisfaction score. Doctors benefited from quicker workflows and earlier treatment adjustments, while patients appreciated the convenience and supportive interaction. 

Apollo 24|7

Apollo 24|7 partnered with Infobip to implement a unified omnichannel communications platform by combining data, chatbots, live chat, messaging, voice and automation. Patients can check symptoms, book appointments or diagnostic tests, get lab results, receive reminders, and get live support via WhatsApp, RCS, voice, or other preferred channels.   

Apollo 24|7 achieved a 95% message delivery rate, reduced call-center workload by 20%, increased diagnostic bookings by 49%, and boosted revenue per order by 72%. The chatbot provided a more convenient and personalized experience, with 67% of sessions actively engaging patients. 

Hospital Alemão Oswaldo Cruz

Hospital Alemão Oswaldo Cruz partnered with Infobip to unify patient communications using chatbots and messaging platforms integrated with its CRM. This automation streamlined appointments and check-ups, boosting WhatsApp bookings by 82%, cutting response times by 63%, and raising patient satisfaction to 96%. 

Limitations and concerns around AI healthcare chatbots and how to mitigate them 

AI healthcare chatbots offer many advantages, but they also present unique challenges and risks that must be carefully managed to ensure patient safety, privacy, and trust. Below are some of the main limitations and concerns, along with practical ways to mitigate them. 

Lack of medical specialization 

Publicly available AI chatbots such as ChatGPT or Perplexity were not designed specifically for medical use. While these platforms include general safety features, they may lack the clinical expertise and tailored logic required for accurate healthcare advice. Using generic chatbots can lead to inappropriate or unsafe recommendations. 

Privacy and data protection risks 

Handling protected health information (PHI) introduces significant privacy concerns. If chatbots are not built with robust security features, sensitive patient data could be exposed, leading to identity theft, violations of privacy laws, or reputational damage. 

Regulatory compliance challenges 

Healthcare chatbots must comply with complex regulations such as HIPAA (in the U.S.), GDPR (in Europe), and other local healthcare data laws. Failure to comply can result in legal penalties and loss of patient trust. 

Safety concerns and scope limitations 

Chatbots must not provide diagnoses or treatment recommendations beyond their approved scope. There is a risk of patients relying too heavily on automated advice, which could result in missed or delayed care for serious conditions. 

Human oversight and escalation 

AI chatbots can struggle with complex, ambiguous, or high-risk cases that require professional judgment. Without effective escalation mechanisms, patients may not receive the care they need. 

Bias and inaccuracy 

AI models can inadvertently reflect biases present in training data or produce inaccurate information if not properly managed. This can compromise the quality of care and erode patient confidence. 

Building trust and transparency 

Patients may be skeptical about the accuracy, privacy, and reliability of chatbot interactions. Lack of transparency can discourage adoption and engagement. 

FAQs about healthcare chatbots and AI