Generative AI in healthcare: Emerging use for care

The types of input that conversational agents can receive and interpret have also expanded, with some conversational agents capable of analyzing movements, such as gestures, facial expressions, and eye movements [11,12]. 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. In the last Section, we will outline some scenarios in which conversational agents could become the primary interface between patients and health services. The second type of active agents is a fully autonomous system acting on behalf of its users.

Further information on research design is available in the Nature Research Reporting Summary linked to this article. Periodic health updates and reminders help people stay motivated to achieve their health goals.

Health Tracking & Management

Those reviews did not differentiate between the type of CAs used besides the AI methods used in each study, so this review focused on investigating the different types of dialogue management with the AI method used in each study. Clarifying the technical features of the AI CAs will help to choose the appropriate type of AI CAs. Regarding limitations, most studies did not include technical performance details, which makes replicability of the studies reviewed problematic. Another limitation of the reviewed literature is the heterogeneity and the prevalence of quasi-experimental studies. A systematic search was performed in February 2021, on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library, not restricted by year or language. Search terms included “conversational agents”, “dialogue systems”, “relational agents”, and “chatbots” (complete search strategy available in Appendix A) [1,6,25,26].

• Overall, about three-quarters of the studies (22/30, 73%) reported positive or mixed results for most of the outcomes.
• These innovations hold great promise for expanding healthcare access, enhancing patient outcomes, and streamlining healthcare systems.
• Positive and mixed outcomes were combined for the final presentation of the data in line with the framework.
• 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.
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• The efficiency of AI in screening and analysis makes it economically viable to pursue treatments for rare or neglected diseases.

While AI chatbots can help to improve patient engagement and communication, they may not always provide accurate or appropriate medical advice in real time. There is also the issue of language barriers and cultural differences, which can limit the effectiveness of AI chatbots in becoming medical professionals in certain contexts. Several studies reported user feedback that was specific to that conversational agent. This included a preference for telephone IVR over web-based pediatric care guidance [9] and a simple avatar with a computer-generated voice over a more life-like agent with a recorded voice [42].

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Four apps utilized AI generation, indicating that the user could write two to three sentences to the healthbot and receive a potentially relevant response. Conversational AI is powering many key use cases that impact both care givers and patients. For both text-based and voice-based systems, it is the data that empowers the underlying engine to deliver a satisfactory response. Basically, conversational AI platforms collect and track patients’ data at scale.

Over time, once they have more experience and confidence in the technology, these organizations may start to use gen AI with clinical applications. The purpose of AI chatbots in healthcare is to manage patient inquiries, provide crucial information, and arrange appointments, thereby allowing medical staff to focus on more urgent matters and emergencies. It also serves as an easily accessible source of health information, lessening the need for patients to contact healthcare providers for routine post-care queries, ultimately saving time and resources. Furthermore, AI can help to proactively ensure that patient data is up-to-date, prompting users to fill in missing or outdated information. Such advanced Conversational AI systems not only lead to a more organized healthcare establishment but also offer patients a smoother, more responsive experience.

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. Health-focused apps with chatbots (“healthbots”) have a critical role in addressing gaps in quality healthcare. There is limited evidence on how such healthbots are developed and applied in practice. Our review of healthbots aims to classify types of healthbots, contexts of use, and their natural language processing capabilities.

All quality assessments were conducted by 2 independent reviewers, with disagreements resolved by consensus. As there was a wide variety of study designs, the study types were classified by 1 reviewer and validated by a second reviewer, with disagreements being resolved by discussion with a third reviewer. As the broad inclusion criteria were intended to conversational ai in healthcare 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. Further, in order to ensure the responsible and effective use of the novel and still-developing technology, ethical concerns and data privacy must be thoroughly addressed.

These AI-driven chatbots serve as virtual assistants to healthcare providers, offering real-time information, decision support, and facilitating seamless communication with patients. Healthcare communication is a multifaceted domain that encompasses interactions between patients, healthcare providers, caregivers, and the broader healthcare ecosystem. Effective communication has long been recognized as a fundamental element of quality healthcare delivery.

In the dynamic realm of healthcare, Artificial Intelligence (AI) has emerged as a game-changer, bringing forth innovative solutions to enhance patient engagement and streamline medical services. Among the remarkable AI applications, healthcare chatbots stand out as virtual assistants, poised to revolutionize the way patients interact with the healthcare ecosystem. These intelligent conversational agents offer a spectrum of services, from scheduling appointments to providing crucial medical information and symptom analysis.

It is possible that the lack of evaluation of the implications of the agents for health care provision and resources was because of an emphasis on technology development and evaluation, rather than system integration. They must also be properly evaluated with a large sample of users, rather than be simply presented as unsubstantiated claims that the agent will reduce costs and save health care providers’ time. Suggestions such as this, that conversational agents have the potential to improve health care provision, save health care providers’ time, and reduce costs, were frequently made in the studies. However, as demonstrated above, very few studies quantified these claims and even fewer measured these outcomes with objective measures. Although many were in the early stages of testing, claims about the potential value to the health care system in terms of time or money should be substantiated.

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Positive and mixed outcomes were combined for the final presentation of the data in line with the framework. However, it might be more useful to distinguish between studies that attempted to find significant evidence for an outcome but did not and those that did not attempt it. This would provide a clearer picture of which outcomes are not being supported by the evidence and should be targeted for improvement, and which outcomes still need to be examined. In the future, it would be worth evaluating whether the coding system should be adjusted to provide a more detailed and informative summary of the evidence. Overall, about three-quarters of the studies (22/30, 73%) reported positive or mixed results for most of the outcomes.

• Use continuous learning modules to ensure the AI stays relevant and in tune with the latest medical knowledge.
• The key lies in ongoing collaboration between AI developers, healthcare professionals, and institutions to ensure these technologies meet the highest standards of accuracy, reliability, and patient care.
• By integrating into these systems, the conversational AI can provide users and patients with more relevant and personalised responses.
• They want to be able to look up coverage and have questions answered without dealing with long hold times or multiple transfers.
• Search terms included “conversational agents”, “dialogue systems”, “relational agents”, and “chatbots” (complete search strategy available in Appendix A) [1,6,25,26].

There are already examples of AI virtual assistants that can perform initial patient assessments by asking relevant questions, collecting information about symptoms, and helping prioritize cases based on urgency. Knowing what your goals are will help you focus your efforts and attention on the specific conversational AI tools that are built to solve those challenges. Otherwise, it’s easy to get overwhelmed by the ever-increasing number of healthcare AI solutions available. Extracting the greatest value from the gen-AI opportunity will require broad, high-quality data sets.

This is also the stage where the bot is integrated with other systems like electronic medical health records, CRMs, omni channel systems and calendars to improve workflows. Such integration is what takes the application from being just an intelligent bot towards becoming a full-purpose concierge that addresses the needs of more internal teams in addition to patients. Once the data preparation is done, it is time to set up the flow of the conversation. This step involves mapping out and curating all the possible answers that the bot can return. The answers can range from simple direct answers to more ambiguous questions involving more complex workflows.

Thirty articles were considered eligible for inclusion in the systematic literature review. Four more papers were excluded during extraction data based on the exclusion criteria. Twenty-six articles were considered eligible for inclusion in the systematic literature review (Figure 1). All authors contributed to the assessment of the apps, and to writing of the manuscript. Only ten apps (12%) stated that they were HIPAA compliant, and three (4%) were Child Online Privacy and Protection Act (COPPA)-compliant. There were only six (8%) apps that utilized a theoretical or therapeutic framework underpinning their approach, including Cognitive Behavioral Therapy (CBT)43, Dialectic Behavioral Therapy (DBT)44, and Stages of Change/Transtheoretical Model45.

To this end, it would be useful for future studies to structure their evaluation of conversational agents around a behavioral change framework (eg, the Behavior Change Wheel framework [59]). This is important not only when evaluating the effectiveness of behavior change-focused conversational agents, but also when determining whether and how the adoption of new conversational agent technology will be successful. The characteristics of the 31 included studies are summarized in Multimedia Appendix 3 [8,9,12-15,32-56].

Your patients expect their healthcare providers to be supportive of their needs throughout their personal care journey. Our Intelligent Virtual Assistant (IVA) lets you truly engage with your patients on the channels of their choice. Trust AI assumes a critical role in navigating complexities, particularly in AI-powered chatbots. Serving as a link between theoretical analytical expressions and the numerical models derived through Machine Learning, Trust AI addresses the challenge of explainability.

It plays a pivotal role in patient education, adherence to treatment plans, early detection of health issues, and overall patient satisfaction. Nevertheless, the advent of the digital age has presented both opportunities and challenges to traditional healthcare communication approaches. On the other hand, conversational AI-based chatbots utilize advanced automation, AI, and Natural Language Processing (NLP) to make applications capable of responding to human language.