Conversational AI in Healthcare: 5 Key Use Cases Updated 2023

The highly capable chips and accelerators of today have transformed the entire digital ecosystem, starting with artificial intelligence. In addition, patients have the tools and information available on their fingertips to manage their own health. Within the first 48 hours of its implementation, the MyGov Corona Helpdesk processed over five million conversations from users across the country.

• While AI-powered chatbots have been instrumental in transforming the healthcare landscape, their implementation and integration have many challenges.
• If even this stage does not produce a response, the bot passes the question back to a live agent.
• Babylon Health’s AI system exemplifies this by handling patient triage and preliminary consultations, freeing up valuable time for healthcare professionals.
• For instance, AI-driven chatbots can provide 24/7 support, answering queries and offering medical advice.

These advancements eliminate unnecessary delays, effectively bridging the gap between diagnosis and treatment initiation. This technology has the potential to combat the spread of inaccurate health information in several ways. Example – in case of a public health crisis like the Covid-19, such a system can disseminate recommended advice about washing hands, social distancing, and covering face with masks. It can also advise patients about when to visit a healthcare facility and how to manage their symptoms. Another significant transformation in healthcare via conversational AI is related to tracking patients’ health. For many patients, visiting a doctor simply means a lack of control over the self while facing severe symptoms because of an underlying health problem.

Automation of Administrative Tasks

In hospitals, AI-powered bots automate routine and repetitive tasks such as taking vitals and delivering medication, freeing healthcare professionals to focus on more complex tasks. Conversational AI in Healthcare has become increasingly prominent as the healthcare industry continues to embrace significant technological advancements over the years to improve patient care. In summary, the impact of AI in drug discovery and research, as exemplified by Atomwise, is profound. By harnessing the power of AI for rapid and accurate screening of drug compounds, the entire landscape of drug development is being reshaped. This technology offers the promise of faster, more efficient, and potentially more innovative approaches to finding treatments for a wide array of diseases, from common illnesses to rare and complex conditions. Both the Cleveland Clinic and Google’s DeepMind exemplify how AI can transform healthcare.

This either prevents them from making the right decisions or actively encourages them to make the wrong ones. Deloitte Chief Futurist Mike Bechtel provides perspective on the excitement around generative AI within the context of Tech Trends’ macro technology forces. Hemnabh Varia is a deputy manager with Deloitte Services India Pvt Ltd, affiliated with the Deloitte Center for Health Solutions.

Post-treatment Care

The efficiency of AI in screening and analysis makes it economically viable to pursue treatments for rare or neglected diseases. These conditions often do not receive the same level of attention in traditional drug discovery due to the high costs and lower financial incentives. Their AI-driven tools help in analyzing medical images more accurately and quickly, aiding radiologists in diagnosing diseases such as cancer with greater precision.

Gen AI can automatically and immediately summarize this data regardless of the volume, freeing up time for people to address more complex needs. It’s being utilized for scheduling appointments, guiding post-treatment care, providing patient support, sending reminders, and even handling billing issues. While it offers efficiency and round-the-clock service, ensuring data privacy and ethical considerations remains crucial during its deployment. Notably, Conversational AI is significantly enhancing the high quality of communication between physicians and patients, and it’s also paving the way for remote patient treatment. But, In the realm of research in medical sciences, artificially intelligent systems have become integral.

Desirable Qualities In Conversational AI

Ninety-six percent of apps employed a finite-state conversational design, indicating that users are taken through a flow of predetermined steps then provided with a response. The majority (83%) conversational ai in healthcare had a fixed-input dialogue interaction method, indicating that the healthbot led the conversation flow. This was typically done by providing “button-push” options for user-indicated responses.

• There has been one systematic review of commercially available apps; this review focused on features and content of healthbots that supported dementia patients and their caregivers34.
• In this regard, a conversation with an AI Assistant would efficiently substitute the initial phone call you might make to your doctor to discuss your concerns, before making an in-person appointment.
• It can raise awareness about a specific health-related concern or crisis by offering swift access to accurate, reliable and timely information.

These often contain several content nodes or steps to qualify the question and lead the user to a specific intent. Lastly, healthcare being a service that is universally accessed, the patient data could also include health details of various influential and political figures. Leakage of such data could find their way into hackers and bad actors who could use such data for nefarious purposes. Secondly, access to such critical data can enable by third party agents could cause embarrassment, be it intentional or not. One of the earliest publicised applications of big data involved a case of a parent being targeted with pregnancy ads for his teenage daughter.

Organization leaders may choose to outsource various parts of their tech stack after evaluating their own internal capabilities. Applying gen AI to healthcare businesses could help transform the industry, but only after leaders take inventory of their own operations, talent, and technological capabilities. Within hospitals and physician groups, gen-AI technology has the potential to affect everything from continuity of care to clinical operations and contracting to corporate functions.

The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents. Figure 2

shows how our conversational agent ‘lives’ on patients’ devices and acts as starting point for checking their health state or requesting health services. The agent provides highly customized recommendations, stores and integrates the patient’s various data points from other sources, such as wearable technologies, and proactively checks on patients’ symptoms and health. It shares data where necessary with the patient’s GP and autonomously books appointments when needed.

Considerations for healthcare practices that are interested in conversational AI

In the next three to four years, as AI systems improve, the focus will inevitably shift toward making these virtual assistants more human at work. This mini-review embarks on an exploration of the profound impact that AI-powered chatbots are exerting on healthcare communication, with a particular emphasis on their capacity to catalyze transformative changes in patient behavior and lifestyle choices. Our journey takes us through the evolution of chatbots, from rudimentary text-based systems to sophisticated conversational agents driven by AI technologies. We delve into their multifaceted applications within the healthcare sector, spanning from the dissemination of critical health information to facilitating remote patient monitoring and providing empathetic support services. The most frequently raised issue with conversational agents (9 studies) was poor understanding because of limited vocabulary, voice recognition accuracy, or error management of word inputs [13,32-37,41,52].

This paper intends to present an architecture adopted to deploy a successful conversational AI agent, named Ainume, using Google Dialogflow on the Google Cloud Platform (GCP). Ainume identifies symptoms of common and chronic diseases, accordingly, suggesting nutraceutical solutions to reduce the symptoms of these diseases. The focus of this paper is on one aspect that Ainume is equipped to deal with, that is, cardiovascular diseases. There were a variety of study types included in this review; so several different quality assessment tools were used to assess the risk of bias and quality of the 31 included studies. A total of 6 studies could not be classified as RCTs, cohort, qualitative, or cross-sectional studies, and their study design was coded as other [12,39,40,44,52,55]. Most of these studies were papers describing the development and initial evaluation of conversational agents, and half of them did not have participants [40,44,55].

The idea to converse with computers using natural language goes back to Alan Turing’s seminal

Imitation Game

[

13

]. He devised what is today known as the

Turing Test

, which challenges people to determine whether they are talking to another human or, in fact, a machine. In 1966, Joseph Weizenbaum developed ELIZA, a chatbot, which he originally created to demonstrate how limited the communication between humans and machines were at the time. Using simple pattern matching and substitutions, the chatbot conveyed the mere illusion of understanding its users. The chatbot took the form of a Rogerian psychotherapist named after Carl Rogers, using his famous method that involved slightly rephrasing and repeating what patients had just said. And while Weizenbaum ended up being surprised by users’ tendency to attribute human-like feelings to the chatbot, today’s systems and devices are often seen as companions and human-like entities with social elements and personalities [

14

].