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Conversational AI in Healthcare: Use Cases, Benefits & Challenges

In this guide, we’ll dive into these use cases and explore the considerations for healthcare practices that are interested in this technology. Leaders must also assess their AI tech stack—including the applications, models, APIs, and other tech infrastructure they currently use—to determine where their technological capabilities will need to be augmented to leverage large language models at scale. Investing in the AI tech stack now will help organizations add more uses for gen AI later. One of the significant advantages of DeepMind’s system is its accuracy and speed in diagnosing conditions that might be challenging even for experienced specialists. The AI’s ability to quickly process and analyze vast amounts of data allows it to identify diseases at earlier stages, which is crucial for timely and effective treatment. A question that many organisations face in their digital transformation journey is that of whether to build technology solutions within the firm, using their own resources or to buy the services of a qualified vendor.

Whether your practice is an early adopter when it comes to healthcare technology or more cautious, it’s not too early to start thinking about the implications of AI and how it can improve patient communications and productivity. Conversational AI is becoming an increasingly important tool for healthcare organizations, and the use cases for this technology are ever expanding. For example, CSAT surveys (customer satisfaction surveys) are one of the most commonly used tools, across all industries, to measure how satisfied clients are with their interactions with a business. Generally, CSAT surveys are sent to clients or patients immediately after an interaction like a support call or a live chat conversation.

Start your conversational commerce journey with Haptik

With this in mind, there are some key guiding principles to follow during testing. Differences in KPIs Between Private and Public Healthcare InstitutionsEven in the healthcare industry, the priorities and KPIs could differ based on the individual institution. Private institutions might prioritize patient satisfaction and high-quality care more, especially for the Executive and Premium packages. They will be interested in KPIs around leads and awareness among users on related treatment services and elective surgeries. However, very few studies discussed the cost-effectiveness (5/30, 17%, coded as positive or mixed) or safety, privacy, and security (14/30, 47%, coded as positive or mixed) outcomes for the agents being evaluated.

As conversational agents are often touted as having the potential to reduce the burden on health care resources, evaluations of the implications of the agents for improved health care provision and reduced resource demand also need to be assessed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations conversational ai in healthcare of the agents highlighted in specific qualitative feedback. It will be important for future studies of conversational agents to take care to properly structure and report their studies to improve the quality of the evidence. Without high-quality evidence, it is difficult to assess the current state of conversational agents in health care – what is working, and what needs to be improved to make them a more useful tool.

Six ways large language models are changing healthcare

Safety aspects of CAs is still a neglected area, and needs to be included as part of core design considerations. The most commonly used method in the included studies was quasi-experimental, which was used in almost half of the included papers. This is aligned with the findings of the previous systematic reviews of CAs in healthcare [1,27].

This aids in making more informed decisions regarding treatments and interventions. Siemens‘ AI-Rad Companion, an AI-based software assistant, supports radiologists by automating routine tasks and providing quantitative data analysis in imaging, which enhances the accuracy of diagnoses and saves significant time. KeyReply is an AI-powered patient engagement orchestrator that is revolutionizing the healthcare space by enabling Healthcare Providers and Insurers to engage with their customers across a variety of online platforms. Some enterprises were able to manage this sudden shift since they had some form of digital customer servicing channels like live chat via instant messaging tools like WhatsApp or their web site or app. This was especially helpful in catering to customers and employees at home who saw an increased utilisation of live chat services by to 2 to 3 times the previous volumes. On-premise (private cloud or local server) deployment requires more time due to various factors.

Generative AI: Force multiplier for human ambitions

It can raise awareness about a specific health-related concern or crisis by offering swift access to accurate, reliable and timely information. All this in an engaging, conversational manner, across a range of digital platforms including websites, social media, messaging apps etc. There can be no substitute for the inspiring efforts of doctors, medics and other healthcare providers, but technology can play a key role in enabling them to focus their energies more effectively and amplifying the impact of their work. While gen AI has the potential to make a transformative impact akin to that of personal computers and the internet, technological innovations like these often take decades to become ubiquitous. Dr. Dhar is vice chair and US Life Sciences and Health Care (LSHC) Industry Leader for Deloitte LLP leading the overall strategic direction for the life sciences and health care practices, including audit, consulting, tax, and advisory services. He helps Governments, Life Sciences and Health Care clients reinvent wellness, address disease, respond to pandemics and tackle health inequities.

These cannot be circumvented and there is no room for improvisation either, as this could lead to legal and regulatory consequences. Labeling is necessary for any NLP system to extract meaning and establish relations between words and entities. To complicate matters, some of the communication that needs to be automated may be carried out through unofficial channels like personal messaging or email. Summary of the quality assessment and judgments of the ‘other’ studies using the Appraisal tool for Cross-Sectional Studies tool.

The sheer number of active cases may already be overwhelming for a regional hospital but monitoring active cases only may not be sufficient. For effective COVID tracing, the broader circle of people who have been in contact with active cases need to be monitored as well. Therefore, the number of people who require regular check-ins increases exponentially as the circle of contacts increases and this makes manual tracking by medical professionals (or other service providers) almost impossible. The COVID-19 pandemic has accelerated the digitization of healthcare services, making this technology more relevant than ever before.

On top of it, many even struggle with the preparation of this data and setting up dialog flow to make the conversation flow seamlessly. This can be addressed by integrating with electronic medical records and other healthcare systems and adopting tools like dbt. An AI Assistant can answer common queries and FAQs related to a particular disease, health condition or epidemic.

• Conversational AI is primed to make a significant impact in the healthcare industry when implemented the right way.
• Though we are still relatively early in AI development stages, the healthcare industry is already beginning to adopt conversational AI in a variety of different ways.
• As the broad inclusion criteria were intended to capture all relevant studies, a few of the included studies used implementation models for artificial AI research that were beyond the scope of classic public health design methods.
• It can lead a patient through a series of questions in a logical sequence to understand their condition that may require immediate escalation.

But in healthcare, where it is often a life or death matter, the stakes are much higher. A parent could be enquiring about the right treatment for her injured child or a user might be in need of urgent emergency care for a stroke. In such high-impact scenarios, chatbots may have to prioritize accuracy and knowledge over other traits like personality. Differences in Symptom Descriptions and Medical TerminologyThe healthcare industry is somewhat unique due to the vast medical terminology it uses. Specifically, there could be a big gap between the language of the user’s queries and the correct medical terms corresponding to those queries. Common queries around location and operating hours aside, users could ask about medical procedures, health screening, symptoms, and matching doctors and could even share their personal info.

Clinical, Legal, and Ethical Aspects of Artificial Intelligence–Assisted Conversational Agents in Health Care

Organizations that can implement gen AI quickly are likely to be in the best position to see benefits, whether in the form of better efficiency or improved outcomes and experience. Back-office work and administrative functions, such as finance and staffing, provide the foundations on which a hospital system runs. But they often operate in silos, relying on manual inputs across fragmented systems that may not allow for easy data sharing or synthesis. Gen AI represents a meaningful new tool that can help unlock a piece of the unrealized $1 trillion of improvement potential present in the industry. Atomwise has partnered with pharmaceutical companies and research institutions, leveraging its AI technology to expedite their drug discovery efforts. These collaborations are not only speeding up the development of new drugs but are also helping in repurposing existing drugs for new therapeutic uses.

• The CAs in the papers used various AI methods such as speech recognition, facial recognition, and NLP.
• Several natural language processing (NLP) platforms, in particular using natural language understanding (NLU), such as Google Dialogflow, IBM Watson and Rasa are used in conversational AI.
• Gray literature that was also identified in those databases (including conference proceedings, theses, dissertations), were included for screening.
• Subsequently, in 1995, Richard Wallace created the Artificial Linguistic Internet Computer Entity (ALICE), an award-winning chatbot capable of processing natural language and engaging in conversations with humans using pattern-matching [

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• Seven were embodied conversational agents (ECA), a virtual agent that appeared on computer screens and was equipped with a virtual, human-like body that had real-time conversations with humans.

The successful integration of AI with existing healthcare systems is paving the way for more efficient, accurate, and personalized patient care. Cerner, another major player in healthcare IT, has been incorporating AI into its electronic health record (EHR) systems. By using predictive analytics, their AI tools help in identifying patients at risk of deteriorating health conditions, thereby enabling early intervention. Just like outpatient care, we can hope to see more conversational AI systems doing the bulk of the first layer of emotional support. This could be in the form of notifications, daily check-ins and gamification of positive habits. Coupled with the growth of wearables and IoT devices, conversational AI systems will enable hospitals to care for patients in their homes before they even have a need to visit.

Generative AI in the Future of Healthcare Industry

Easy access to and the ability to keep track of patients’ conversations and data allows these agents to personalize the information and information delivery to an unprecedented degree. If the agent has access to the patient’s clinical and health services history and, once authorized, the system does not need to repeatedly request patients’ credentials as is the case with current consultations over the phone. This can save considerable time and conveys the idea to the patient of having a personal health coach literally “in their pocket”. Often anthropomorphic elements, such as a human-like avatar or natural language use, make interactions more humane and personal.

Perceived ease of use or usefulness (27/30, 90%), the process of service delivery or performance (26/30, 87%), appropriateness (24/30, 80%), and satisfaction (26/31, 84%) were the outcomes that had the most support from the studies. Just over three-quarters (23/30, 77%) of the studies also reported positive or mixed evidence of effectiveness. During the screening process, studies of conversational agents that were not capable of interacting with human users via unconstrained NLP were excluded. These included conversational agents that only allowed users to select from predefined options or agents with prerecorded responses that did not adapt to subsequent user responses. The basis for this exclusion is that, without the capability of using NLP, computational methods and technologies are rudimentary and do not advance the aims of AI for autonomous computational agents. As many studies did not explicitly state whether the investigated agent was capable of NLP, a description in the paper of the conversational agent allowing free-text or free-speech input was used as an indicator for NLP, and these studies were included.

The study designs also varied widely, with 29% (9/31) using cross-sectional designs, 26% (8/31) using RCTs, 23% (7/31) using qualitative methods, 19% (6/31) using cohort studies, and 1 using a cluster crossover design. The full data extraction table is available in Multimedia Appendix 4 [8,9,12-15,32-56]. Data were extracted by 1 reviewer, and key data points from the studies, specified in the protocol and identified on further study of the publications, were recorded in a spreadsheet and validated by a second reviewer. The data extraction form was based on the minimum requirements recommended by the Cochrane Handbook for Systematic Reviews [27].

Those pre-recorded voice commands invoke our custom Google Action (voice applications). This setup allows us to run experience sampling surveys, which provide subjective user assessments throughout the day [

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]. To gauge the users’ current context, the speaker asks about people’s availability, boredom level, mood, and current activities. Invoking the survey is done in regular intervals but, with the help of sensor data, surveys can be triggered by certain events as well, such as the presence of a person, when the user wakes up in the morning or before leaving their home. For patients living with chronic health conditions, specific types of mini-surveys and reminders can be implemented in voice applications and be deployed on our system to collect data about patients’ medical or mental conditions and support medication adherence.

Every interaction with an AI chatbot contributes to its understanding of the patient. It’s available 24/7 to provide valuable, digestible information on managing her condition. It also requires transparent communication to consumers interacting with the AI chatbots and employees for swift technology adoption. On the side of medical staff, employees can send updates, submit requests, and track status within one system in the form of conversation.