Here’s How AI Chatbots Are Simplifying Health Care Choices for Aging Adults

This relates to a feeling of unease when interacting with something that seems eerily human (Ciechanowski et al., 2019). The primary outcomes were changes in the levels of COVID-19 vaccine confidence and acceptance from the pre- and post-intervention questionnaires. Confidence was measured using the Vaccine Confidence Index TM (VCI)72, which has been used in the context of COVID-19 vaccinations73,74 and included perceptions of the importance, effectiveness, and safety of COVID-19 vaccines. VCI was recorded on a 5-point Likert scale (ranging from 5-strongly agree, 4-agree, 3-neither agree nor disagree, 2-disagree, to 1-strongly disagree) and the pre- and post-intervention differences were categorized into a 3-point scale of “improved”, “no change”, and “declined”. For example, if a participant answered “Agree” in the pre-intervention questionnaire and “Strongly Agree” in the post-intervention questionnaire, the participant would score 1-point. Positive, zero, or negative differences were categorized into “improved”, “no change”, or “decreased” outcomes, respectively.

The Token Limit metric evaluates the performance of chatbots, focusing on the number of tokens used in multi-turn interactions. The number of tokens significantly impacts the word count in a query and the computational resources required during inference. As the number of tokens increases, the memory and computation needed also increase63, leading to higher latency and reduced usability.

For instance, in 2023, the National Eating Disorder Association shut down its chatbot, Tessa, after it inappropriately provided healthy eating resources and weight loss tips to patients seeking help for eating disorders. The TCS study includes compelling success stories that illustrate the real-world impact of AI in healthcare. For example, a Swedish firm has integrated AI into its operations, achieving significant benefits such as a 20% improvement in forecast accuracy and increased revenue from AI-driven product-service bundles.

Overcoming The Challenges Of Using AI Chatbots In Healthcare

The giveaway comes in how the applicant speaks, thinks out loud, and responds to unexpected questions. However, Chuck also notes that applicants can “keep prompting it [the program] to add some personality” and reflection. Medical school administrators who have experimented with chatbots say the prose is clear, well-organized, and knowledgeable about their institutions.

In addition, the increase in patient waiting time and lack of efficient patient management across the globe also boosts the growth of healthcare chatbots market. Furthermore, growth potential offered by rise in awareness during the forecast period offer lucrative opportunities for the growth of the market. While providers work to build out their digital front doors, they’re more concerned with patient access tools—provider search or online check-in—than the systems that can help patients self-triage.

The human touch and clinical expertise of healthcare providers remain essential in delivering comprehensive, empathetic, and high-quality care. According to Ram and Sheth (1989), functional barriers are the constraints of innovative technologies that require changes in users’ established behavioral habits, norms, and traditions. They include three dimensions of individual perceptions relating to usage barriers, value barriers, and risk barriers regarding innovation. This study measured the perceived functional ChatGPT App barriers of people’s resistance to health chatbots in terms of the three dimensions mentioned above. Furthermore, a study utilized deep learning to detect skin cancer which showed that an AI using CNN accurately diagnosed melanoma cases compared to dermatologists and recommended treatment options [13, 14]. Researchers utilized AI technology in many other disease states, such as detecting diabetic retinopathy [15] and EKG abnormality and predicting risk factors for cardiovascular diseases [16, 17].

• Importantly, there were some factors that could sway a patient to place greater trust in generative AI chatbots.
• Further studies could advance the generalizability of chatbot interventions to target seniors, instead of their guardians, directly, and also investigate whether improve confidence in vaccine effectiveness could be translated into vaccination actions.
• AI chatbots also have a global reach, making mental health support accessible to individuals in remote or underserved areas.

A noteworthy example is TytoCare’s telehealth platform, where AI-driven chatbots guide patients through self-examination procedures during telemedicine consultations, ensuring the integrity of collected data (9). They have become versatile tools, contributing to various facets of healthcare communication and delivery. Chatbots embedded in healthcare websites and mobile apps offer users real-time access to medical information, assisting in self-diagnosis and health education (5). 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.

MEDICAL IMAGING

Based on technologies like Alexa or Siri, the medical counterparts can interpret symptoms, suggest resources, and provide emotional support to caregivers. The healthcare chatbot sector in Japan is expected to experience rapid expansion, forecasting a CAGR of 25.2% by 2034. The country commitment to technological ChatGPT innovation in healthcare, coupled with a growing demand for virtual health assistance, positions Japan as a significant player in the global chatbot market. There is currently no legal or regulatory framework that would justify AI tools taking on significant, autonomous roles in healthcare.

The integration of CURATE.AI into the clinical workflow showed successful incorporation and potential benefits in terms of reducing chemotherapy dose and improving patient response rates and durations compared to the standard of care. These findings support the need for prospective validation through randomized clinical trials and indicate the potential of AI in optimizing chemotherapy dosing and lowering the risk of adverse drug events. Today, AI is transforming healthcare, finance, and transportation, among other fields, and its impact is only set to grow. In academia, AI has been used to develop intelligent tutoring systems, which are computer programs that can adapt to the needs of individual students. These systems have improved student learning outcomes in various subjects, including math and science.

To acknowledge all the results and provide a more nuanced response, we have divided the discussion into three parts. Finally, we discuss the strengths and limitations of this study to achieve a precise and balanced conclusion. As chatbots continue to develop, healthcare organizations will be faced with the decision to embrace or reject these technologies. In the future, the authors predicted that AI chatbot developers will work directly with healthcare providers to develop HIPAA-compliant chat functionalities.

DUOS is also free but is more specialized and only available as a health plan benefit for Medicare Advantage members. • Limit the chatbot’s access to sensitive records so that it can interact only with data it needs. If the answers show areas of concern, the chatbot can respond with a series of escalating steps, such as making an appointment for an office visit or alerting a clinician to call the patient for detailed follow-up and a decision about next steps.

In particular, in the healthcare domain, where safety and currentness of information are paramount, hallucinations pose a significant concern. The evaluation of healthcare chatbots should encompass not only their ability to provide personalized responses to individual users but also their ability to offer accurate and reliable information that applies to a broader user base. Striking the right balance between personalization and generalization is crucial to ensure practical and trustworthy healthcare guidance. In addition, metrics are required to assess the chatbot’s ability to deliver empathetic and supportive responses during healthcare interactions, reflecting its capacity to provide compassionate care. Moreover, existing evaluations overlook performance aspects of models, such as computational efficiency and model size, which are crucial for practical implementation. AI chatbots represent a significant advancement in mental health support, offering numerous benefits such as increased accessibility, reduced stigma, and cost-effectiveness.

This is especially important for dementia patients and caregivers, who keep increasing as the population ages, and face care challenges daily,” said Hristidis. Australia healthcare chatbot market is anticipated to grow, projecting a CAGR of 27.4% by 2034. The country focus on technological advancements in healthcare, coupled with a proactive approach to patient-centered care, positions Australia as a key contributor to the global expansion of healthcare chatbot applications.

AI chatbot technologies will also be able to supplement other technologies such as electronic medical records in other verbally intensive medical situations, such as creating transcripts during an examination or procedure. In keeping these records, the technology can help properly time patient visits as well as the handing off of patients from one doctor or nurse to another at the end of shifts. The rapid advancement of AI in healthcare raises important regulatory and ethical considerations. According to the TCS study, 81% of leaders have called for a global set of regulations and standards on AI. Establishing clear guidelines and ethical frameworks is crucial to ensure responsible AI use and protect patient privacy and data security. Robotics-assisted surgeries, such as endovascular neurosurgery, are becoming more common, offering higher precision and reducing the risk of complications.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research is supported by the Fundamental Research Funds for the Central Universities (23JNQMX50). Several measures must be taken to ensure responsible and effective implementation of AI in healthcare. Any disagreements or concerns about the literature or methodology were discussed in detail among the authors. Indexed databases, including PubMed/Medline (National Library of Medicine), Scopus, and EMBASE, were independently searched with notime restrictions, but the searches were limited to the English language.

This study systematically and empirically explored the factors and psychological mechanisms that influence people’s resistance to health chatbots by constructing a parallel mediation model. The study extends our understanding of individuals’ acceptance behaviors toward medical AI technologies from the perspective of their formative resistance behavioral tendency. Second, by combining the IRT and PWM, this study enriches existing literature on the antecedents and psychological pathways of individuals’ resistance to health chatbots. Prior research has primarily emphasized the impact of rational considerations such as acceptability (Boucher et al., 2021), perceived utility (Nadarzynski et al., 2019), and performance expectancy (Huang et al., 2021), on individuals’ health chatbot adoption behavior.

One stream of healthcare chatbot development focuses on deriving new knowledge from large datasets, such as scans. This is different from the more traditional image of chatbots that interact with people in real-time, using probabilistic scenarios to give recommendations that improve over time. Similarly, companies such as Antara Health use chatbots to create customized treatment plans based on each patient’s unique medical history and data. These chatbots reduce the administrative burden on healthcare teams by managing multiple patient interactions, freeing up medical staff to focus on more complicated cases. Among these tools, AI chatbots stand out as dynamic solutions that offer real-time analytics, revolutionizing healthcare delivery at the bedside. These advancements eliminate unnecessary delays, effectively bridging the gap between diagnosis and treatment initiation.

LLM-chatbots are known for their remarkable conversational skills and highly convincing responses, and experts fear that the integration of LLM-chatbots into search engines may increase users’ confidence and dependency in the information given by a chatbot. Prof. Stephen Gilbert, Professor for Medical Device Regulatory Science at Else Kr ner Fresenius Center for Digital Health at Technische Universit t Dresden (TU Dresden), is not in favour of using current LLM-chatbots in healthcare. For example, some patients ask if a specific reading on recent bloodwork should concern them, says Christopher Longhurst, MD, chief medical officer and chief digital officer at UC San Diego benefits of chatbots in healthcare Health. The chatbot drafts an answer that says the reading is in the normal range, or, if the reading is of concern, that someone will contact them about next steps (such as an appointment or making a plan to adjust their diet). One goal of the chatbot services, Oppenheim says, is to reduce readmissions by guiding patients to stay on track with their care at home and intervening early if there are signs of worsening health problems. The technology has the potential to improve patient health by guiding them through complex medication schedules, keeping clinicians routinely updated about a patient’s condition, and enabling clinicians to step in at early signs of trouble.

This is particularly concerning in healthcare, where the chatbot’s predictions may influence critical decisions such as diagnosis or treatment (23). Given the potential for adverse outcomes, it becomes imperative to ensure that the development and deployment of AI chatbot models in healthcare adhere to principles of fairness and equity (16). Achieving this can promote equitable healthcare access and outcomes for all population groups, regardless of their demographic characteristics (20). Federated learning is an emerging research topic that addresses the challenges of preserving data privacy and security in the context of machine learning, including AI chatbots. It allows multiple participants to collaboratively train a machine learning model without sharing their raw data. Instead, the model is trained locally on each participant’s device or server using their respective data, and only the updated model parameters are shared with a central server or coordinator.

Importantly, there were some factors that could sway a patient to place greater trust in generative AI chatbots. Particularly, the researchers found that after patients got positive provider reviews of the technologies, they were more comfortable using the tools. In 2021, a team led by the University of Washington developed a chatbot to gather information on social needs among emergency department (ED) visitors.

AI systems can monitor and measure quality metrics in real time, ensuring that healthcare providers adhere to the highest standards. By analyzing vast amounts of patient data, AI can identify patterns and suggest evidence-based interventions, enabling personalized care that improves patient outcomes and enhances the overall quality of care. One of the most promising applications of AI in healthcare is its ability to enhance diagnostic accuracy. AI-powered algorithms can analyze medical images with precision that often surpasses human capabilities.

Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare. These technologies are especially valuable for accelerating clinical trials by improving trial design, optimizing eligibility screening and enhancing recruitment workflows. Further, AI models are useful for advancing clinical trial data analysis, as they enable researchers to process extensive datasets, detect patterns, predict results, and propose treatment strategies informed by patient data. Piloting the comparative analysis of the AI statements with the key messages of other chapters revealed that the number and focus of the statements (AI outputs) differed greatly from those of the key messages and overlapped. Therefore, the sole use of ChatGPT as an accurate and reliable information resource for healthcare professionals seems insufficient.

In order to drive optimisation and ensure consistently high-quality responses, OpenAI employs a combination of both supervised learning and reinforcement learning with human feedback (RLHF) [20]. Supervised learning involves training the model on a vast corpus of text data with labelled examples of inputs and outputs. The model is trained to predict the output given the input, and the weights of the model are adjusted to minimise the discrepancy between the predicted output and the actual output. On the other hand, RLHF involves training the model to maximise a reward signal on the basis of its actions. The evaluation of the response quality by a human evaluator helps refine the model’s responses in an iterative process over time [21]. Healthcare chatbots provide businesses with a range of benefits, ranging from saving time and resources to improving customer service and expanding functionality.

The increasing role of AI in healthcare also makes it a prerequisite to have adequate curriculum-based training and a continuing education program on AI applications to (mental) healthcare and AI-based interventions that can be accessed by all. The IRT provides a comprehensive operationalization framework for examining individual resistance to innovative technologies (Kleijnen et al., 2009). Previous research has demonstrated that functional and psychological barriers can significantly predict people’s resistance intentions and behaviors toward innovative technologies. For example, the IRT explains approximately 60% of the variance in people’s resistance to mobile payment technology (Kaur et al., 2020) and nearly 55% of the variance in their resistance to the online purchase of experience goods (Lian and Yen, 2013). Specifically, in terms of functional barriers, Prakash and Das (2022) discovered that the perceived value barriers, usage complexity, and privacy disclosure risks of digital contact-tracking apps can increase the intentions to resist such devices. Singh and Pandey (2024) also indicated that inefficient collaboration with AI devices is also a critical barrier to their usage.

Preferred method for initial medical consultation between stigmatizing and embarrassing health conditions

“Although some of the burden of purging PHI from chat inputs falls on the querying clinician, covered entities can take measures to create environments that prevent inadvertent PHI disclosure. You can foun additiona information about ai customer service and artificial intelligence and NLP. At a minimum, covered entities should provide training specifically on chatbot risks, beginning now and continuing in the context of annual HIPAA training,” the article continued. This task may be tempting to complete with the help of an AI chatbot, but doing so without a BAA in place may expose patient data. “The innovation—and risk—with an AI chatbot therefore does not lie with its AI engine but with its chat functionality,” the article suggested.

The United States healthcare chatbot market is poised for substantial growth, with a projected CAGR of 21.8% by 2034. Factors such as increased adoption of digital health solutions, emphasis on patient engagement, and advancements in artificial intelligence contribute to the market positive trajectory. Despite AI’s immense potential, the study also highlights several challenges organizations face in AI implementation.

Concurrently, it is important that future investigations on chatbot interventions to enhance vaccine confidence include underrepresented minority groups, such as migrant workers, to broaden the applicability and scalability of chatbots. In this study, we test the effectiveness of COVID-19 chatbots on people who were unvaccinated or had delayed vaccinations until the government vaccine mandates in Thailand, Hong Kong, and Singapore. Among many factors affecting vaccine attitudes and behaviours, we focus on participants’ changes in levels of vaccine confidence and acceptance, before and after using the COVID-19 chatbots. Given differentiating findings per target populations, we encourage future studies to further substantiate the evidence of vaccine chatbots’ effectiveness in advancing vaccine confidence and acceptance. In the review article, the authors extensively examined the use of AI in healthcare settings.

Additionally, determining relevant clinical metrics and selecting an appropriate methodology is crucial to achieving the desired outcomes. Human contribution to the design and application of AI tools is subject to bias and could be amplified by AI if not closely monitored [113]. The AI-generated data and/or analysis could be realistic and convincing; however, hallucination could also be a major issue which is the tendency to fabricate and create false information that cannot be supported by existing evidence [114]. Thus, the development of AI tools has implications for current health professions education, highlighting the necessity of recognizing human fallibility in areas including clinical reasoning and evidence-based medicine [115].

Experts say health chatbots could have a big impact on the healthcare business, but their varying levels of accuracy raise critical questions about their potential to support or undermine patient care. The UK’s National Health Service (NHS) has embraced the power of AI-powered chatbots to revolutionize patient triage. Their “NHS 111 Online” chatbot provides patients with advice and guidance for their health concerns. Using natural language processing, the chatbot understands patients’ symptoms and offers personalized recommendations based on the severity of their condition.

Whereas, for consulting with a chatbot, participants reported preferring to interact via text and least preferred video, avatar-based interactions. This is interesting given the increase in avatar-based chatbots as a method to increase their uptake and acceptance in healthcare settings (Ciechanowski et al., 2019; Moriuchi, 2022). Familiarity will also play a role as users will tend to prefer technology they are more familiar with (e.g., chatbot text based interaction compared to speech based avatar interaction). Previous research in a consumer setting found that people perceive an increased social presence when a chatbot is more “human-like,” making consumers more likely to feel socially judged (Holthöwer and van Doorn, 2022). Furthermore, research suggests that users may show a preference for text chatbots over video-based chatbots due to the “uncanny valley” effect.

The key concerns, their examples, and strategies to address concerns are shown in Table 2 (Denecke et al., 2021; Cosco, 2023; Nothwest Executive Education, 2023; Sengupta, 2023; Wang et al., 2023). Among those who believe AI will make bias and unfair treatment based on a patient’s race or ethnicity worse, 28% explain their viewpoint by saying things like AI reflects human bias or that the data AI is trained on can reflect bias. Another reason given by 10% of this group is that AI would make the problem worse because human judgment is needed in medicine. These responses emphasized the importance of personalized care offered by providers and expressed the view that AI would not be able to replace this aspect of health care. Chatbots can be used for memory training or to stimulate recollections for patients suffering from dementia, and for caregivers, chatbots can provide advice and emotional support.

However, it also addresses the significant challenges posed by the integration of AI tools into healthcare communication. For instance, Babylon Health’s chatbot can evaluate symptoms and provide medical advice, guiding patients on whether to consult a doctor. Sensely’s chatbot, equipped with an avatar, helps users navigate their health insurance benefits and connects them directly with healthcare services. Instead, they serve as valuable tools to assist and augment medical staff’s capabilities. Chatbots can handle routine inquiries, provide initial triage, and offer general health advice, freeing healthcare professionals to focus on more complex cases and personalized patient care.

AI bolsters the capabilities of these solutions by helping to predict complications, allowing care teams to preemptively intervene in cases of clinical deterioration, and flagging patients who are likely to benefit from hospital-at-home services compared to inpatient care. These AI tools can also be applied to clinical needs, using patient symptom data to provide care recommendations. AI chatbots are emerging as a potential solution to this conundrum, as they are well-suited to sorting through patient needs and providing resources in certain areas.

Extensive training with a custom set of medical data helps a chatbot provide accurate answers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.