Conversational AI in Healthcare: Use Cases, Benefits & Challenges

This involves tweaking the chemical structure of these hits to improve their efficacy, reduce potential side effects, and ensure their suitability as a drug. Atomwise’s AI can simulate and predict the outcomes of these modifications, thereby streamlining the lead optimization process. Summary of the quality assessment and judgments of qualitative studies using the CASP (Critical Appraisal Skills Programme) Qualitative Study Checklist.

It can also suggest when someone should attend a healthcare institution, when they should self-isolate, and how to manage their symptoms. Advanced conversational AI systems also keep up with the current guidelines, ensuring that the advice is constantly updated with the latest science and best practices. Conversational AI combines advanced automation, artificial intelligence, and natural language processing (NLP) to enable robots to comprehend and respond to human language. These applications already create value to the tune of billions of dollars either in cost savings or increased revenues. Furthermore, as these applications continue to improve their ability to learn and act, they will generate even more improvements in precision, efficiency, cost savings, and better healthcare outcomes.

• It should be noted that the AXIS tool used to assess the other studies was designed for cross-sectional studies and does not fit exactly with the designs of these studies.
• Conversational AI may diagnose symptoms and medical triaging and allocate care priorities as needed.
• On the other hand, the same system can be used to streamline the patient onboarding process and guide them through the process in an easy way.

Various administrative tasks are handled in healthcare facilities on a daily basis, most of which are carried out inefficiently. For example, medical staff members have to search for countless patient forms and switch between applications, resulting in loss of time and frustration. 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. Other than the in-person consultation with health experts, what they need is easy access to information and tools to take control of their health. Managing appointments is one of a healthcare facility’s most demanding yet vital tasks.

Higher-quality studies—with more consistent reporting of design methods and better sample selection—are also needed to more accurately assess the usefulness and identify the key areas of improvement for current conversational agents. A more holistic approach to the design, development, and evaluation of conversational agents will help drive innovation and improve their value in health care. First, are the conversational agents investigated effective at achieving their intended health-related outcomes, and does the effectiveness vary depending on the type of agent?

How conversational AI is transforming healthcare

One of the more interesting new discoveries is the emergence of artificial intelligence systems such as conversational AI for healthcare. When a user asks the chatbot a question, it goes through the NLP engine for processing and response generation. If a reply is unavailable, the bot seamlessly transfers the query to a live agent who can now interact with the customer and continue the conversation without affecting their experience.

Setting goals and objectives for conversational AI implementation in the healthcare industry involves defining specific actions such as improving patient engagement, reducing administrative workload, and improving care delivery efficiency. Conversational AI implementation requires coordination between IT teams and healthcare professionals, who must frequently monitor and evaluate the technology’s performance. Such information ensures that it continues to accomplish its objectives while also catering to patient demands. Another driver of the demand for conversational AI healthcare applications is the COVID-19 pandemic, specifically stay-at-home measures, social distancing norms, and the increasing pivot towards deferred care. Instead, they turned to digital care solutions like telehealth and chatbots to alleviate their problems. Our vision is for conversational AI to become a core tool providing the right information at the right time to both healthcare professionals and patients.

With natural language understanding, WALi has automated conversations with staff to resolve issues instantly. Powered by large language models (LLMs), these copilots possess deep knowledge across various systems and domains across a healthcare organization. They can respond contextually to a wide array of possible questions and conversations.

Visual output, in this case, included the use of an embodied avatar with modified expressions in response to user input. Eighty-two percent of apps had a specific task for the user to focus on (i.e., entering symptoms). To facilitate this assessment, we develop and present an evaluative framework that classifies the key characteristics of healthbots.

How accurate are AI and chatbots in diagnosing and providing medical advice?

Despite the large body of research concerning the application of conversational agents in health care, most reviews have limited their focus to a particular health area, agent type, or function [10,19-22]. Although there are a few recent systematic reviews that have examined a more comprehensive scope, they have presented an overall synthesis of the body of knowledge. One review developed a taxonomy that described the architecture and functions of conversational agents in health care and the state of the field but did not evaluate the effectiveness, usability, or implications for users [5]. Another systematic review investigated the outcome measures of the studies of conversational agents but limited the inclusion criteria to agents that used natural language input and had been tested with human participants [2]. Additionally, their initial database searches only retrieved 1531 articles, which raises the concern that some relevant articles may have been overlooked [2]. Their search was updated in February 2018, but given the rapid pace of technological development, there is a need to provide an update and expansion to these previous systematic reviews.

Most of the studies (5/8) had a high risk of performance bias, but this was predominantly because blinding was not possible given the nature of the intervention. 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. About a quarter of studies (8/30, 27%) had neither positive nor mixed reported evidence for more than half of the SF/HIT outcomes. The synthesis framework for the assessment of health information technology (SF/HIT) was used to structure the evaluation of the studies because it included a whole system set of outcome variables [31]. These included effectiveness, satisfaction, and perceived ease of use or usefulness, among others. In accordance with the framework, evidence for each of the outcome variables was coded as positive or mixed or neutral or negative.

The Future of Healthcare with Conversational AI

For doctors, AI’s analytical capabilities provide access to structured dashboards where all information gathered about each patient finds its home. Adherence rates, medication numbers, and treatment check-ins are all available with a single click for each patient. Patients frequently have pressing inquiries that require immediate answers but may not necessitate the attention of a staff member. The good news is that most customers prefer self-service over speaking to someone, which is good news for personnel-strapped healthcare institutions. 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.

We aimed to examine how conversational agents have been employed and evaluated in the literature to date and map out their characteristics. Finally, in line with the observed gaps in the literature, we sought to provide recommendations for future conversational agent research, design, and applications. Despite limitations in access to smartphones and 3G connectivity, our review highlights the growing use of chatbot apps in low- and middle-income countries.

AI chatbots that have been upgraded with NLP can interpret your input and provide replies that are appropriate to your conversational style. AI-based chatbots can not only handle larger call volumes, but they can also provide a more consistent user experience with every interaction. In the U.S. and elsewhere, healthcare is fast evolving into a more consumer-driven industry.

This approach eliminates the need for employees to dig through separate knowledge bases or siloed documents. By unifying access to tribal knowledge, Lumi resolves issues in seconds without any back and forth. Conversational AI delivers fast, personalized support, allowing over 26K healthcare employees to focus on patient care instead of tech hurdles. Even with these challenges, the benefits are immeasurable to the industry and serve to provide positive impacts on patients, healthcare practitioners, and organizations.

By deploying Moveworks‘ copilot, MGB gave nurses, doctors, and other frontline staff an instant self-service IT solution right within the tools they already use. This solution saves precious time they can devote to patient care instead of IT frustrations. The conversational AI assistant Chat PG unites fragmented information so that employees get the knowledge they need when they need it. Powered by Moveworks‘ AI engine, WALi required no lengthy setup or manual dialog creation. Employees simply chat with WALi to get passwords reset, ask HR questions, or address other requests.

Further research should also analyze the cost-effectiveness, privacy, and security of the agents. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another. Overall, 17 articles reported on conversational agents that focused on treatment, monitoring, or rehabilitation of patients with specific conditions. One study reported on a conversational agent to help preserve cognitive abilities in those with Alzheimer disease [62].

Moreover, such platforms also offer more privacy and a record of interactions – two benefits that users appreciate and even prefer. The other studies performed best on the questions about whether the study design was appropriate for the aims and whether the conclusions were justified by the results (6/6 yes for both). They also did well, overall, on the appropriate choice of outcome variables and internal consistency (5/6 yes for both). Furthermore, only 1 study adequately addressed the questions about the use of previously assessed outcome measures (1/5 yes), sufficient description of the methods for replication (1/6 yes), and discussion of study limitations (1/6 yes).

These broad inclusion criteria were established to enable an assessment of a wide range of applications of conversational agents. There were no restrictions on study type, as long as a conversational agent was evaluated, and intervention and observational studies such as cross-sectional surveys, cohort studies, and qualitative studies were included. Intervention studies were not required to have a specific comparator or any comparator. For conversational agents to be successful in health care, it is crucial to understand the effectiveness of current agents in achieving their intended outcomes.

Post-treatment Care

For each app, data on the number of downloads were abstracted for five countries with the highest numbers of downloads over the previous 30 days. Chatbot apps were downloaded globally, including in several African and Asian countries with more limited smartphone penetration. The United States had the highest number of total downloads (~1.9 million downloads, 12 apps), followed by India (~1.4 million downloads, 13 apps) and the Philippines (~1.25 million downloads, 4 apps). Details on the number of downloads and app across the 33 countries are available in Appendix 2.

These synonyms were generated using a web-based search and by identifying specific terms or phrases used in the titles of articles discussing health care conversational agents. The reference list of relevant articles and systematic reviews were also searched for further articles related to the review. Identifying and characterizing elements of NLP is challenging, as apps do not explicitly state their machine learning approach. We were able to determine the dialogue management system and the dialogue interaction method of the healthbot for 92% of apps.

The choice of WhatsApp as a platform was a key factor in ensuring the wide reach of this solution, given that WhatsApp is the world’s largest messaging platform, with over 400 million users in India alone. Conversational AI implementation requires organisations to comply with various data regulations and data security guidelines. The worldwide pandemic has made us all realise the fact that misinformation spreads even faster than a virus and can cause real damage to people.

HIPAA Compliance for the Healthcare Industry

Studies examining screening or diagnosis agents and treatment support agents had the highest average number of positive or mixed outcomes (mean 10, SD 0.6, and mean 9, SD 1.2, respectively). Treatment support agents had primary functions that included empowering patients to engage more fully in clinical appointments, encouraging attending screenings for health care conditions, and supporting patient self-management. In contrast, mental health agents focused on addressing challenges related to depression, anxiety, and alcohol abuse, among others. However, given the small number of studies for each category of agents, these comparisons should be interpreted with caution. We identified 12 studies with conversational agents for healthy lifestyle behavior change in the general population as well as overweight and obese individuals.

The core of its technology lies in using artificial intelligence to predict how different chemical compounds will interact with specific targets, such as proteins or enzymes within the human body. This process involves analyzing the molecular structure of countless compounds conversational ai in healthcare and predicting their effectiveness in binding to these targets, a crucial step in developing effective drugs. 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.

Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies.

Dialogue management is the high-level design of how the healthbot will maintain the entire conversation while the dialogue interaction method is the way in which the user interacts with the system. While these choices are often tied together, e.g., finite-state and fixed input, we do see examples of finite-state dialogue management with the semantic parser interaction method. 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%) 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.

Healthcare organizations implementing conversational AI are seeing improvements in efficiencies, access, costs, retention, and patient satisfaction by automating routine administrative tasks, freeing up staff for higher-value work, and more. By reducing wait times, the Cleveland Clinic’s AI system indirectly contributes to improved patient outcomes. Faster response times in critical situations https://chat.openai.com/ can significantly increase the chances of successful treatment and recovery, especially in emergency and high-risk cases. Healthcare organizations must conduct regular audits to ensure that their AI systems comply with all relevant laws and regulations. This includes staying updated with changes in legislation, evolving cybersecurity threats, and advancements in AI technology.

Overall, 19 studies reported on conversational agents used to support or complement existing health care services. Other studies discussed conversational agents automating health care services such as patient history taking [48,77], providing health advice [83], symptom checking [58], and triaging and diagnosis support [60,69,74]. Conversational AI is changing how healthcare providers engage with patients by utilizing natural language processing (NLP) and machine learning (ML). From booking appointments to monitoring conditions, conversational AI has multiple uses that improve the healthcare experience for both patients and clinicians. In this article, let’s look at the top 10 use cases of conversational AI in healthcare and considerations for effective implementation. We used an extensive list of 63 search terms, including various synonyms for conversational agents (Multimedia Appendix 1).

Our assessment indicated that only a few apps use machine learning and natural language processing approaches, despite such marketing claims. Most apps allowed for a finite-state input, where the dialogue is led by the system and follows a predetermined algorithm. Healthbots are potentially transformative in centering care around the user; however, they are in a nascent state of development and require further research on development, automation and adoption for a population-level health impact. This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library.

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. 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.

It was one of the first few AI agents developed for human interaction and entertainment and introduced the shift from text- to voice-operated conversational agents. Soon after, ALICE gained plenty of attention in 1995, after which it went on to win the Loebner Prize 3 times in 2000, 2001, and 2004. Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care. In addition to data and conversation flow, organizations developing conversational AI chatbots should also focus on including desirable qualities, such as engagement and empathy, to create a more positive user experience. You can foun additiona information about ai customer service and artificial intelligence and NLP. While conversational AI systems cannot replace human care, with the right qualities, they can augment the healthcare staff’s efforts by automating repetitive tasks and offering initial emotional support.

Of the included studies, 22 articles focused on conversational agents with long-term goals and 23 with short-term goals (Multimedia Appendix 3 [30,44-89]). Two studies reported on conversational agents with both short-term and long-term goals [45,56], for example, answering immediate queries (short) and providing education and increasing users’ knowledge on the topic over time (long) [56]. Conversational agents with short-term scope provided users with a response or service almost instantaneously, such as answering health-related queries [84]. Conversely, those with long-term scope needed to build a relationship with the user, over time, to help them overcome health-related issues such as smoking cessation [72] or working through a mental health problem [80].

AI-driven chatbots and other applications are helping to usher in a digital revolution in the healthcare industry. In particular, conversational AI in healthcare is transforming patient care, support, and reducing the burden for healthcare providers. From providing information and scheduling to diagnosis and engagement, AI in healthcare revolutionizes patient experiences and improves outcomes for both consumers and providers. Unlike simple chatbots, conversational AI utilizes advanced natural language processing, machine learning, and AI to enable natural, human-like interactions between computer systems and human users. This technology is poised to revolutionize healthcare delivery by streamlining workflows, improving access, and enhancing patient engagement. When grouped by the agent’s health care scope, studies of certain types of agents appear to do better than others (Table 3).

On the other hand, some patients described the embodied CAs as annoying (39%) and boring (30%). Another study for diabetes [40] illustrated the user experience through attractiveness (0.74), perspicuity (0.67), and efficiency (0.77), by using the scale of Cronbach’s Alpha Coefficient correlation. In mental health, a study for treatment and education reported that some users felt the chatbot was hard to engage with and had no availability to ask questions [33].

It assists patients by providing timely appointment reminders, informing them about documents they should (or needn’t) bring, and whether they might need someone’s assistance after the appointment. Within the first 48 hours of its implementation, the MyGov Corona Helpdesk processed over five million conversations from users across the country. The need to educate people about the facts behind a particular health-related issue, and to undo the damage caused by misinformation, does place an additional burden on medical professionals.

For this, regulators should establish a robust data security framework as well as ethical guidelines for the training and use of these systems. The COVID-19 pandemic has accelerated the digitization of healthcare services, making this technology more relevant than ever before. The authors would like to thank the outreach librarians Liz Callow (University of Oxford) and Kirsten Elliot (Imperial College London), for their assistance in developing search terms and reviewing search strategies. EM’s work on digital health solutions is currently supported by the Sir David Cooksey Fellowship in Healthcare Translation at the University of Oxford.

However, this ranged significantly, with usability, agent performance, and satisfaction having the most support across the studies, and cost-effectiveness receiving hardly any support. It should also be noted that the definitions of effectiveness were highly varied and, as evidenced by the methodological limitations identified in the quality assessment, rarely evaluated with the scrutiny expected for medical devices. Although the results reported are promising for the use of conversational agents in health care, there are a number of limitations in both the studies analyzed and the structure of this review that questions the validity of this finding. 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].