Medical Chatbots - Use Cases, Examples and Case Studies of Conversational AI in Medicine and Health

Emerging trends like increasing service demand, shifting focus towards 360-degree wellbeing, and rising costs of quality care are propelling the adoption of new technologies in the healthcare sector. By harnessing the power of Conversational AI, medical institutions are rewriting the rules of patient engagement. We are witnessing a rapid upsurge in the development and implementation of various AI solutions in the healthcare sector.

Gartner predicts that nearly 75% of all global healthcare delivery organizations (HDOs) will have invested in an AI capability by 2021¹

The market is brimming with technology vendors working on AI models and algorithms to enhance healthcare quality. However, the majority of these AI solutions (focusing on operational performance and clinical outcomes) are still in their infancy. All, except for medical chatbots.

What are medical chatbots?

Medical chatbots are AI-powered conversational solutions that help patients, insurance companies, and healthcare providers easily connect with each other. These bots can also play a critical role in making relevant healthcare information accessible to the right stakeholders, at the right time.

From enhancing patient experience and helping medical professionals, to improving healthcare processes and unlocking actionable insights, medical or healthcare chatbots can be used for achieving various objectives. Poised to change the way payers, medical care providers, and patients interact with each other, medical chatbots are one of the most matured and influential AI-powered healthcare solutions developed so far.

Get inspired by these 6 innovative medical chatbots:

  • Your.MD

    Designed to help patients find the healthcare information they need to stay healthy, MD offers various features and functions. However, what stands apart from all the other features is its symptom checker. Available on the web and as a stand-alone app, it acts as a personal health assistant allowing users to check symptoms, ask a question, or take a health quiz.

    Pros: The chatbot uses a vast repository of reliable healthcare information to offer relevant answers to all user queries. And best of all, it is available for free.

    Cons: Language understanding capabilities are limited in this chatbot, which negatively impacts the experience.

  • Sensely

    Sensely helps insurers, healthcare service providers, businesses, and pharma companies build custom solutions to bridge the gap between low fidelity chatbots and high-quality human communications. From underwriting, claims handling, and symptom assessment to customer service, employee wellness, and clinical trials, it offers a wide range of capabilities. It is important to understand that Sensely is not a chatbot; instead, it offers the platform and pre-built capabilities required to build different types of medical chatbots.

    Pros: It is possible to build a unique brand persona using various characters with Sensely.

    Cons: We are still a long way from truly embodying empathy with AI. While the concept is good, its practical implications remain to be seen.

  • NINA (AskNestlé)

    Built using Senseforth’s conversational AI platform A.ware, NINA is one of India’s first AI-powered digital nutritionists. NINA helps young parents create a daily meal plan for their children. Parents can customize the meal plan to address specific nutritional requirements, find innovative recipes to make the fussy eaters happy, set reminders, and even keep a food journal. Parents can interact with Nina on Google Assistant or the website. The bot handles around 50,000 queries each month.

    Pros: Information provided by the chatbot is very interesting and contextual.

    Cons: Content has been curated keeping Indian audience in mind. Hence, a lot more contextualization is required to cater to the needs of a global audience.

  • Ada

    This medical chatbot brings together doctors, scientists, and industry pioneers to improve the quality of personal healthcare. Instead of banking only on a repository of information, Ada compares user queries with thousands of similar cases. It then analyzes its findings against the information available in the medical library to craft a much more relevant and contextual response.

    Pros: Ada puts access in the hands of the users.

    Cons: Again, limited natural language understanding capabilities negatively impact the user experience.

  • MAGe

    Unlike the majority of patient-focused medical chatbots, MAGe caters to the requirements of internal stakeholders across a healthcare service provider company. It analyzes comments by visitors/ patients on different platforms like Facebook, Twitter, MouthShut, etc. MAGe also provides detailed analytics and segregates comments on the basis of positive and negative sentiment filters. It enables different stakeholders to view or download reports for different regions and hospitals. This chatbot is built using pre-existing industry models offered by Senseforth’s AI platform A.Ware.

    Pros: Provides actionable insights to the right stakeholders at the right time.

    Cons: Data capture points are limited.

  • SafedrugBot

    This is another good example of how a simple solution like a chatbot can make a huge difference. SafedrugBot offers guidance to medical practitioners about the usage of drugs by breastfeeding mothers. From understanding the side effects of various drugs to finding safer alternatives, it helps doctors tap into a vast knowledge hub to augment their capabilities. This chatbot is available on Telegram.

    Pros: SafedrugBot helps doctors easily verify drug-related information.

    Cons: It’s available on only one messaging platform and fails to deliver an omnichannel experience.

What can we expect in the future?

While the industry is already flooded with various healthcare chatbots, we still see a reluctance towards experimentation with more evolved use cases. This is partly because Conversational AI is still evolving and has a long way to go. As natural language understanding and artificial intelligence technologies evolve, we will see the emergence of more sophisticated healthcare chatbot solutions.

There is no doubt that the accuracy and relevancy of these chatbots will increase as well. But successful adoption of healthcare chatbots will require a lot more than that. It will require a fine balance between human empathy and machine intelligence to develop chatbot solutions that can address today’s healthcare challenges.

In the coming few years, we can expect to see healthcare chatbots that can:

  • Serve as 24/7 companions, monitor health status in real-time, and automatically call for assistance in case of an emergency.
  • Help manage chronic conditions, mental health issues, and behavioral and psychological disorders.
  • Proactively identify symptoms, crosscheck them against medical history, suggest the next steps, and improve the treatment success rate in cases where early diagnosis can play a critical role.
  • Make self-care easier by acting as a virtual assistant and providing timely medical advice.

However, technology is only one side of the coin. We have welcomed conversational AI solutions in our lives with open arms for activities like playing music and adjusting lights in our living rooms. Will we be able to show the same level of trust when it comes to healthcare chatbots?

While building futuristic healthcare chatbots, companies will have to think beyond technology. They will need to carefully consider various factors that can impact the user adoption of chatbots in the healthcare industry. Only then will we be able to unlock the true power of AI-enabled conversational healthcare.

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