The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review PMC

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. Different technologies have supported CAs, including independent platforms, apps delivered via web or mobile device, short message services (SMS), and telephone (Table 2).

There were 47 (31%) apps that were developed for a primary care domain area and 22 (14%) for a mental health domain. Involvement in the primary care domain was defined as healthbots containing symptom assessment, primary prevention, and other health-promoting measures. Additionally, focus areas including anesthesiology, conversational ai in healthcare cancer, cardiology, dermatology, endocrinology, genetics, medical claims, neurology, nutrition, pathology, and sexual health were assessed. As apps could fall within one or both of the major domains and/or be included in multiple focus areas, each individual domain and focus area was assigned a numerical value.

Today’s customers demand better plans, more personalized care, low-cost solutions, and high-quality, accurate information from the healthcare providers they interact with. These organizations need better ways to provide high-quality consumer experiences while lowering their costs to keep up with these demands. By predicting patient influx and identifying peak hours, the system allows hospital administrators to allocate resources more efficiently — such as staffing, bed availability, and emergency room readiness. This optimization leads to a more balanced workload for healthcare providers and shorter wait times for patients. The AI system implemented at the Cleveland Clinic utilizes a sophisticated algorithm that analyzes incoming patient data.

Top 10 use cases of conversational AI in healthcare

Currently, most bots available on app stores are patient-facing and focus on the areas of primary care and mental health. Only six (8%) of apps included in the review had a theoretical/therapeutic underpinning for their approach. Two-thirds of the apps contained features to personalize the app content to each user based on data collected from them. Seventy-nine percent apps did not have any of the security features assessed and only 10 apps reported HIPAA compliance.

Further limitations of this review are that we limited the focus to include only unconstrained NLP and interaction. This was chosen as a focus because of the advantages NLP offers for simulating human-to-human interaction. However, it may have excluded studies of relevant conversational agents that could be satisfactory, useful, and effective in addressing current health care challenges. Additionally, no spidering searches were used to identify potentially relevant studies in the references of the included studies that were missed in the initial search. The exclusion of conference abstracts might also have caused relevant papers that were classified as abstracts to be missed; however, a previous systematic review that included conference abstracts in their search only had 1 included in their final selection [2].

Another limitation of the reviewed literature is the heterogeneity and the prevalence of quasi-experimental studies. In 3 studies, conversational agents were used for healthy behavior change, specifically targeting smoking cessation, alcohol misuse treatment, and physical activity promotion [72,73,89]. For smoking cessation, participants indicated enjoyment when conversing with the conversational agent, and effectiveness was also insinuated by 38.3% reporting not having smoked in the past week and 69.4% admitting to a reduction in smoking frequency [72]. In the study by Elmasri et al [73], the participants (young adults) reported a higher satisfaction rate with the use of the conversational agent to manage and treat alcohol misuse. For physical activity promotion through the use of a reflection companion, response rates were high (96% at baseline, 90% at follow-up), insinuating high engagement throughout the study.

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. By reducing wait times, AI helps in managing hospital resources efficiently and improving patient satisfaction. In diagnosis, AI’s ability to rapidly analyze complex medical data can lead to earlier and more accurate detection of diseases, potentially saving lives.

• 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.
• Details on the number of downloads and app across the 33 countries are available in Appendix 2.
• For instance, ecosystem stakeholders’ traditionally slow approach to adopting new technologies restricts access to training data, making it difficult to get the NLP and ML-driven systems up and running.

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. When AI systems are used for research or data analysis, it’s crucial to anonymize patient data. This means stripping away personally identifiable information to ensure that individual patients cannot be traced from the data. Techniques like differential privacy can be employed, where the AI analyzes patterns in the data without exposing individual data points, further safeguarding patient privacy. 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.

Mental health support and counseling

Summary of the quality assessment and judgments of the cross-sectional studies using the Appraisal tool for Cross-Sectional Studies tool. While the benefits of Conversational AI systems are numerous, there are also potential drawbacks and challenges to existing systems that must be taken into consideration. These include ethical considerations and concerns surrounding the use of Conversational AI without human intervention in sensitive healthcare settings. Based on the information given, the AI virtual assistant can advise on seeking immediate medical attention, scheduling appointments, or considering at-home remedies. Additionally, this ensures standardized guidance rooted in established medical protocols, streamlining patient care. While Conversational AI holds immense potential to transform the healthcare industry, there are several drawbacks and challenges that must be considered.

Section 3 addresses the results that include descriptions of included studies, CAs, AI methods, and evaluation measures. Chronic diseases are one of the biggest healthcare challenges of the 21st century [17,18]. Once present, they often persist throughout a person’s life, so there is generally a need for long-term management by individuals and health professionals” [1]. Additionally, chronic conditions reduce one’s quality of life and increase healthcare expenses through disability, repeated hospitalization, and treatment procedures. According to the World Health Organization statistics of 2020, non-communicable diseases (e.g., hypertension, diabetes, and depression) and suicide are still prevalent reasons for death in 2016 [19].

• 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.
• The AI system implemented at the Cleveland Clinic utilizes a sophisticated algorithm that analyzes incoming patient data.
• 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 changing how healthcare providers engage with patients by utilizing natural language processing (NLP) and machine learning (ML).

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]. Related to this issue, as the conversational agents often had to ask questions more than once to be able to process the response, users in 3 studies noted disliking the repetitive conversations with the agents [13,36,37]. Both of these issues are key areas of improvement for future research and development of conversational agents because they represent limitations in the usability of the agents in a real-world context. Prior systematic literature reviews explored a variety of CAs in general health care [1,6,24] and aspects of the personalization of health care chatbots using AI [25].

Whether it’s generating detailed invoices or resolving claims issues, AI does so by integrating with existing healthcare systems, ensuring accuracy and a unified patient experience. Two studies employed an emotionally sensitive conversational agent for mental health counselling and general health information advice [83,88]. In the study by Liu et al [83], the sympathetic conversational agent was rated more positively than the advice-only condition.

However, it is equally not uncommon to find many systems with a complex UI that can get frustrating for patients. Next to answering patients’ queries, appointment management is one of the most challenging yet critical operations for a healthcare facility. While it is easy to find appointment scheduling software, they are quite inflexible, leading patients to avoid using them in favor of scheduling an appointment via a phone call. New and improved Artificial Intelligence (AI) techniques are the result of rapid growth in computing abilities that enable machines to learn with least human supervision.

Furthermore, it is important to explore the integration of conversational agents into the existing health systems and services. A hybrid system, where digital technology supplements health care services, is increasingly seen as the optimal solution [103]. This mirrors our acknowledgment that conversational agents will be most advantageous in supporting rather than substituting health care professionals. In most studies, conversational agents were developed and presented independently, unsupported by humans, and separate from the existing health care delivery models, which may prove unsustainable in the long run.

Furthermore, we were unable to extract data regarding the number of app downloads for the Apple iOS store, only the number of ratings. This resulted in the drawback of not being able to fully understand the geographic distribution of healthbots across both stores. These data are not intended to quantify the penetration of healthbots globally, but are presented to highlight the broad global reach of such interventions.

Two other studies focused on conversational agents to provide support and treatment for metabolic conditions such as type 2 diabetes [70] and obesity [46]. One study each reported on the use of a conversational agent for monitoring patients with asthma [85], HIV [45], heart failure [82], and chronic respiratory disease management [63]. Non–disease-specific conversational agents were used as a health information advisor [83] and pediatric generic medicine consultant [65]. Goal-oriented conversational agents were further divided into those that yielded long- or short-term outcomes.

AI in healthcare encompasses tools and machines that can sense and comprehend human inputs, act according to these inputs and their context, and even learn over time to improve their ability to feel, understand and operate. Moreover, unlike legacy AI algorithms and tools that only complement human activities and can’t Chat PG function independently, Conversational AI tools can work independently to augment human activities (e.g., call center interactions). Powered by natural language understanding, Lumi breaks down requests, maps them to relevant solutions across Luminis’ many enterprise systems, and delivers personalized answers.

This leads to better diagnosis and treatment planning while reducing the time patients spend in the scanner. Epic Systems, a leading medical records company, has integrated AI to streamline workflows and enhance patient outcomes. The integration of Conversational AI in healthcare is reshaping the landscape of patient care and medical administration. This article delves into the reasons behind its rising popularity and the methods of its application, backed by real-world examples. Almost two-thirds of the studies (19/31) used samples of less than 100 participants or items of analysis (eg, voice clips and clinical scenarios) with a median sample size of 48 across all the studies.

They serve as a supplemental tool to provide guidance and information based on pre-programmed responses or machine learning algorithms. In simple terms, Conversational AI refers to solutions like chatbots and virtual assistants that employ AI techniques like Natural Language Processing (NLP), voice technology, and Machine Learning (ML) to automate user interactions. These tools go beyond simple rule-based answers to analyze human speech (or text), understand their intent and meaning, and generate appropriate responses.

As technology evolves, the potential for AI in healthcare is boundless, promising a future of enhanced patient care and operational efficiency. Smart hospital rooms equipped with conversational AI technology can improve patient experiences and outcomes. Voice-activated devices can adjust lighting and temperature, control entertainment systems, and call for assistance. They can also provide patients with health information about their care plan and medication schedule. The intricacies of billing, insurance claims, and payments can be a source of stress. Conversational AI, by taking charge of these processes, ensures clarity and efficiency.

In the analysis, outcomes were initially coded separately as positive, mixed, positive or mixed (for studies that reported a positive outcome but did not provide sufficient statistical evidence), and neutral or negative. 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.

When we talk about the healthcare sector, we aren’t referring solely to medical professionals such as doctors, nurses, medics etc. but also to administrative staff at hospitals, clinic and other healthcare facilities. They might be overtaxed at the best of times with the sheer volume of inquiries and questions they need to field on a daily basis. Appointment scheduling and management systems are a common part of healthcare facilities nowadays.

Furthermore, conversational AI may match the proper answer to a question even if its pose differs significantly across users and does not correspond with the precise terminology on-site. Artificial Intelligence in the medical field already has numerous applications that are changing the face of healthcare worldwide. Chatbots can respond to all commonly asked questions and thus take the burden off call centers and their human agents. Instead, they can focus on higher-value tasks and situations where automation cannot work, and their unique human capabilities are required. The healthcare sector can undoubtedly benefit tremendously from such AI-driven customer care automation. Leading healthcare institutions have already implemented AI-powered copilots in partnership with Moveworks to improve their organizations’ day-to-day and the patients they serve.

Most included studies evaluated task-oriented AI CAs (23 studies out of 26) that are used to assist patients and clinicians through specific processes. The majority of the included studies were focused entirely on designing, developing, or evaluating AI CAs that are specific to one chronic condition. This finding implies that AI CAs evolve to provide tailored support for specific chronic conditions, rather than general interventions for a broad range of chronic conditions. To map out the current conversational agent applications in health care, we included primary research studies that had conducted an evaluation and reported findings on a conversational agent implemented for a health care–specific purpose. A further point of exclusion was articles with poorly reported data on chatbot assessments where there was minimal or no evaluation data.

This was a groundbreaking contribution to the field of AI and was reported to have a positive impact on patients who communicated with the conversational agent [13]. A step up from ELIZA was achieved when PARRY, a conversational agent representing a simulated paranoid patient with schizophrenia, was developed [14,15]. Conversational AI in the medical field is helping to bring about much-needed digital transformation with potential benefits for everyone across the healthcare value chain. By allowing users to interact with providers via voice or text-based chatbots and virtual assistants, Conversational AI technology is helping to streamline and automate many different processes. In fact, if implemented correctly, they can transform the delivery of medical services and significantly impact human lives in the next 5 years. Compared to prior reviews focused on AI CAs for healthcare, we found only two review studies that targeted AI CAs for chronic conditions, where one of them focused on voice-based CAs only.

How Can Conversational AI Help the Healthcare Industry?

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. Conversational AI helps gather patient data at scale and glean actionable insights that enable healthcare professionals to improve patient experience and offer personalized care and support. From ancient syringes to the advanced telemedicine of today, healthcare technology has come a long way and has conversational AI as a part of the next exciting developments. As per Accenture’s analysis on this subject, the key clinical healthcare AI applications have the potential to create annual savings of $150 billion by 2026 for the U.S. healthcare economy.

Conversational AI has the potential to enable governments and institutions to establish a reliable source of information about the virus’s transmission. For example, in the case of a public health crisis such as COVID-19, a conversational AI system may distribute recommended advice such as washing your hands for 20 seconds, maintaining social distance, and wearing a face covering. The number of interactions patients have with healthcare experts varies significantly depending on their stage of treatment. For example, post-treatment patients may have frequent check-ups with a doctor, but they are otherwise responsible for following their post-treatment plan.

One article [78] reported on a conversational agent personality that was criticized for being overly formal, and some articles did not report on the personality of the conversational agent at all [30,44,45,50,51,54,55,59,63,67,69,75,77,82-84,87]. 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. Care providers can use conversational AI to gather patient records, health history and lab results in a matter of seconds.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Additionally, such bots also play an important role in providing counselling and social support to individuals who might suffer from conditions that may be stigmatized or have a shortage of skilled healthcare providers. Many of the apps reviewed were focused on mental health, as was seen in other reviews of health chatbots9,27,30,33. At a basic level, conversational AI allows for natural, human-like interactions between computers and users.

In this article, we’ll explore how Conversational AI, powered by Natural Language Processing (NLP), is reshaping healthcare. We’ll outline its pros and cons, touch on the challenges of adding it to current Conversational AI systems, and discuss what the future might hold for this technology. Specifically, Conversational AI systems involve the use of chatbots and voice assistants to enhance patient communication and engagement. While the technology offers numerous benefits, it also presents its fair share of drawbacks and challenges. Only ten apps (12%) stated that they were HIPAA compliant, and three (4%) were Child Online Privacy and Protection Act (COPPA)-compliant. However, Conversational AI will get better at simulating empathy over time, encouraging individuals to speak freely about their health-related issues (sometimes more freely than they would with a human being).

Conversational Artificial Intelligence in Healthcare

Beyond diagnosis assistance, IBM Watson has been used in personalizing patient care plans, especially in oncology, by analyzing a vast array of medical literature and patient data to suggest tailored treatment options. Utilizing AI for predictive analysis in patient care, they have developed systems that can predict patient trajectories and outcomes. GE’s AI-powered applications in medical imaging equipment, like MRI and CT scanners, help in optimizing scan protocols and improving image quality.

Due to this accessibility, conversational agents are also a promising tool for the advancement of patient-centered care and can support users’ involvement in the management of their own health [17,18]. Conversational agents first emerged as a tool in health care in 1966, with the development of a virtual psychotherapist (ELIZA) that could provide predetermined answers to text-based user input [6]. In the decades since, the capabilities of NLP have significantly progressed and aided the development of more advanced AI agents. Many different types of conversational agents that use NLP have been developed, including chatbots, embodied conversational agents (ECAs), and virtual patients, and are accessible by telephone, mobile phones, computers, and many other digital platforms [7-10]. 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].

The examples covered in this article represent just early steps on the path toward a more profound transformation of healthcare through conversational AI. We encourage readers to learn more about the possibilities from our company and others pioneering these leading-edge technologies. Healthcare organizations have unique needs and challenges when implementing conversational AI. It’s crucial to thoroughly evaluate platforms before investing, as deploying new technology like AI carries risks.

Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being

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. To our knowledge, no review has been published examining the landscape of commercially available and consumer-facing healthbots across all health domains and characterized the NLP system design of such apps. This review aims to classify the types of healthbots available on the app store (Apple iOS and Google Play app stores), their contexts of use, as well as their NLP capabilities.

Bots like WoeBot provide mental health support, demonstrating how AI bridges the gap in healthcare accessibility. In simple terms, conversational AI is a category of AI-driven solutions that automate human-like conversations with users. It utilizes techniques like natural language processing and machine learning to tap into their learnings and deliver clear answers to varied questions in a conversational tone.

Quasi-experimental demonstrates the involvement of real-world interventions, instead of artificial laboratory settings. It allows the research to move with higher internal validity than other non-experimental types of research. In addition, quasi-experimental design requires fewer resources and is less expensive compared with RCT.

Twenty of these apps (25.6%) had faulty elements such as providing irrelevant responses, frozen chats, and messages, or broken/unintelligible English. Three of the apps were not fully assessed because their healthbots were non-functional. Conversational AI allows patients to stay on top of their physical health by identifying symptoms early and consulting healthcare professionals online whenever necessary. Enterprises have successfully leveraged AI Assistants to automate the response to FAQs and the resolution of routine, repetitive tasks. A well-designed conversational assistant can reduce the need for human intervention in such tasks by as much as 80%. This enables firms to significantly scale up their customer support capacity, be available to offer 24/7 assistance, and allow their human support staff to focus on more critical tasks.

Future research studies should provide more detailed accounts of the technical aspects of the CAs used. This includes developing a comprehensive and clear taxonomy for the CAs in healthcare. More RCT studies are required to evaluate the efficacy of using AI CAs to manage chronic conditions. Safety aspects of CAs is still a neglected area, and needs to be included as part of core design considerations. Helpfulness, satisfaction, and ease of use were the common features in more than half of the included studies. Regarding diabetes–type 2 [28], a study reported the feedback from patients through various measures, such as competency (85%), helpfulness and friendliness (86%).

It is used to deliver scalable, less costly medical support solutions that can help at any time via smartphone apps or online [8,9]. For example, support and follow-up for adults after cancer treatment via chatbot reduced the patients’ anxiety without needing a psychiatrist [10,11,12]. Hence, CAs can play an useful role in health care, improving consultations by assisting clinicians and patients, supporting consumers with behavior change, and assisting older people in their living environments [13,14,15]. They can also help in completing specific tasks such as self-monitoring and overcoming obstacles for self-management, which is important in chronic disease management and in the fight against pandemics [6,16]. The first conversational agent ELIZA was developed by Weizenbaum [12] in 1966, with ELIZA taking on the role of a person-centered Rogerian psychotherapist (Figure 1).

We see a future where conversational AI enables efficient operations, empowers healthcare workers, and ultimately helps drive better outcomes. Conversational AI has immense potential to continue transforming healthcare in the years ahead. As the underlying natural language processing technology advances, we can expect even more sophisticated applications across the industry. With an understanding of these considerations, healthcare organizations can overcome industry-specific challenges and successfully unlock the benefits of conversational AI, from cost savings to improved workflows and patient experiences. The integration of Conversational AI in healthcare is not just a trend but a necessity. Its ability to enhance patient engagement, streamline administrative tasks, and improve healthcare access is transforming the industry.

Although our review identified 12 articles with a geographical focus in Asia, the evidence stemming from middle-income countries was scarce, and there were no studies from a low-income country. However, digital health initiatives are becoming more common in developing countries, often with a different, context-specific scope, such as ensuring access to health care using social media [102]. To ensure safe and effective use of solutions developed in HIC settings, there is a need for more research to corroborate the safety, effectiveness, and acceptability of these agents in LMICs too.

Many patients ask pressing questions that require immediate response without demanding the attention of a healthcare professional. The answers to these FAQs, if delivered via a self-service knowledge base, can satisfy frequent queries. A research study on customer experience confirms that 92% of consumers would prefer using a knowledge base for self-support if available.

This systematic review introduced a list of AI CAs in healthcare for chronic disease. It reflects the efficiency, acceptability, and usability of the AI CAs in the daily education of, and support for, chronic disease patients. Our review reflected this as most of the included studies were published after 2016 (21 papers).

But, In the realm of research in medical sciences, artificially intelligent systems have become integral. Their prevalent applications encompass patient diagnosis, comprehensive drug discovery and development, and even the transcription of medical documents such as prescriptions. Finally, there is the challenge of integrating Conversational AI with existing healthcare systems and workflows. This requires significant investment in resources and infrastructure, as well as buy-in from healthcare providers and administrators. Without proper planning and execution, the adoption of Conversational AI in healthcare could create more problems than it solves. One of the major concerns regarding Conversational AI in the healthcare sector is the potential of breaching patient privacy.

To maintain compliance, working with knowledgeable vendors specializing in HIPAA-compliant solutions and conducting regular audits is critical. Conversational AI has the potential to aid both doctors and patients in terms of medication management and adherence. In healthcare, AI-powered chatbots evaluate your patients’ lifestyle behaviors, preferences, and medical history to produce tailored daily reminders and guidance. In certain situations, conversational AI in healthcare has made better triaging judgments than certified professionals with a deeper examination of patients’ symptoms and medical history. It means that a user may ask the chatbot a question and get a quick response without waiting for someone to assist.

AI like that used by Atomwise can delve into chemical spaces and structures that might be too complex or time-consuming for traditional methods. This opens up new avenues in drug research, allowing scientists to explore a broader range of potential therapies for various diseases. The company employs advanced machine learning algorithms that learn from vast datasets of chemical structures and their known biological activity. By training these models on existing data, Atomwise’s AI can extrapolate and predict the behavior of new, untested compounds. In addition, patients have the tools and information available on their fingertips to manage their own health.

Applications that only sent in-app text reminders and did not receive any text input from the user were excluded. Apps were also excluded if they were specific to an event (i.e., apps for conferences or marches). Conversational AI has turned into https://chat.openai.com/ an optimal self-service method for the healthcare industry. For example, many users find it difficult to search for relevant answers via the search function on websites if their queries do not involve the same terminology as in existing FAQs.

With a global penetration rate of 96% [28], mobile phones are ubiquitous and avidly used, and can be efficiently harnessed in health care [30]. Conversational agents are increasingly used in diverse fields, including health care, and there is a need to identify different ways and outcomes of the use of conversational agents in health care. Other reviews report solely on the technical aspects of conversational agents such as system architecture and dialogues [38] or on the funding component of health care conversational interfaces [39]. 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. Similarly, there is a gap in the evidence regarding the health economics of these agents.

In the ever-changing world of technology, where innovation knows no limit, only a few things have evoked as much awe as the exponential growth of computing. The highly capable chips and accelerators of today have transformed the entire digital ecosystem, starting with artificial intelligence. And I.M.; writing—original draft preparation, A.B.S.; writing—review and editing, A.B.S., B.N., A.A., A.M., I.M., J.S., B.Y., D.P., M.P.