Chatbots in healthcare: an overview of main benefits and challenges

As an interdisciplinary subject of study for both HCI and public health research, studies must meet the standards of both fields, which are at times contradictory [52]. Methods developed for the evaluation of pharmacological interventions such as RCTs, which were designed to assess the effectiveness of an intervention, are known in HCI and related fields [53] to be limited in the insights they provide toward better design. Chatbots are becoming a trend in many fields such as medical, service industry and more recently in education.

For instance, a Level 1 maturity chatbot only provides pre-built responses to clearly stated questions without the capacity to follow through with any deviations. Most patients prefer to book appointments online instead of making phone calls or sending messages. A chatbot use of chatbots in healthcare further eases the process by allowing patients to know available slots and schedule or delete meetings at a glance. Healthcare Chatbot is an AI-powered software that uses machine learning algorithms or computer programs to interact with leads in auditory or textual modes.

How much does a healthcare chatbot cost?

Rapid diagnoses by chatbots can erode diagnostic practice, which requires practical wisdom and collaboration between different specialists as well as close communication with patients. HCP expertise relies on the intersubjective circulation of knowledge, that is, a pool of dynamic knowledge and the intersubjective criticism of data, knowledge and processes. Our review suggests that healthbots, while potentially transformative in centering care around the user, are in a nascent state of development and require further research on development, automation, and adoption for a population-level health impact. Seventy-four (53%) apps targeted patients with specific illnesses or diseases, sixty (43%) targeted patients’ caregivers or healthy individuals, and six (4%) targeted healthcare providers.

These categories are not exclusive, as chatbots may possess multiple characteristics, making the process more variable. Textbox 1 describes some examples of the recommended apps for each type of chatbot but are not limited to the ones specified. Personalization was defined based on whether the healthbot app as a whole has tailored its content, interface, and functionality to users, including individual user-based or user category-based accommodations. Furthermore, methods of data collection for content personalization were evaluated41. Personalization features were only identified in 47 apps (60%), of which all required information drawn from users’ active participation.

Data hacking

Considering their capabilities and limitations, check out the selection of easy and complicated tasks for artificial intelligence chatbots in the healthcare industry. Some experts also believe doctors will recommend chatbots to patients with ongoing health issues. In the future, we might share our health information with text bots to make better decisions about our health. With so many algorithms and tools around, knowing the different types of chatbots in healthcare is key. This will help you to choose the right tools or find the right experts to build a chat agent that suits your users’ needs.

Due to the small numbers of papers, percentages must be interpreted with caution and only indicate the presence of research in the area rather than an accurate distribution of research. One of the authors screened the titles and abstracts of the studies identified through the database search, selecting the studies deemed to match the eligibility criteria. The second author then screened 50% of the same set of identified studies at random to validate the first author’s selection. The papers meeting the criteria for inclusion at the title- and abstract-screening stage were retrieved and reviewed independently by both authors, with subsequent discussion about discrepancies and resolution to end with an agreed upon list of included studies.

Healthcare providers are relying on conversational artificial intelligence (AI) to serve patients 24/7 which is a game-changer for the industry. Chatbots for healthcare can provide accurate information and a better experience for patients. Considering these numbers, the cybersecurity issue is acute and goes far beyond securing chatbots.

• The latter aspect could explain why cancer is slowly becoming a chronic disease that is manageable over time [19].
• Additionally, we examine the metrics that have been used to evaluate these chatbots, which include subjective ones like the usability and acceptability by the users, and objectives ones, like their accuracy and users’ skills evaluation.
• According to the analysis from the web directory, health promotion chatbots are the most commonly available; however, most of them are only available on a single platform.
• Consequently, under the HIPAA Rule, every person involved in developing or managing your AI assistants that can access, handle, or store PHI at any given time must be HIPAA-compliant making it a must for healthcare app development related projects in general.

The ability to accurately measure performance is critical for continuous feedback and improvement of chatbots, especially the high standards and vulnerable individuals served in health care. Given that the introduction of chatbots to cancer care is relatively recent, rigorous evidence-based research is lacking. Standardized indicators of success between users and chatbots need to be implemented by regulatory agencies before adoption.

Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care

The removal of options may slowly reduce the patient’s awareness of alternatives and interfere with free choice [100]. Most would assume that survivors of cancer would be more inclined to practice health protection behaviors with extra guidance from health professionals; however, the results have been surprising. Smoking accounts for at least 30% of all cancer deaths; however, up to 50% of survivors continue to smoke [88]. The benefit of using chatbots for smoking cessation across various age groups has been highlighted in numerous studies showing improved motivation, accessibility, and adherence to treatment, which have led to increased smoking abstinence [89-91].

The results show a substantial increase in the interest of chatbots in the past few years, shortly before the pandemic. Half (16/32, 50%) of the research evaluated chatbots applied to mental health or COVID-19. The studies suggest promise in the application of chatbots, especially to easily automated and repetitive tasks, but overall, the evidence for the efficacy of chatbots for prevention and intervention across all domains is limited at present. Design-wise, ease of development, ease of access, and platform penetration have likely contributed to the prevalence of web-based and social media chatbots. For example, the built-in modules for chatbot design on social media platforms allow for fast and easy development. Coupled with the popularity of social media, this fueled deployment on social media apps such as Line, WhatsApp, Facebook Messenger, WeChat, and Telegram.

Data synthesis

Hyro is an adaptive communications platform that replaces common-place intent-based AI chatbots with language-based conversational AI, built from NLU, knowledge graphs, and computational linguistics. Doctors also have a virtual assistant chatbot that supplies them with necessary info – Safedrugbot. The bot offers healthcare providers data the right information on drug dosage, adverse drug effects, and the right therapeutic option for various diseases. Forksy is the go-to digital nutritionist that helps you track your eating habits by giving recommendations about diet and caloric intake. This chatbot tracks your diet and provides automated feedback to improve your diet choices; plus, it offers useful information about every food you eat – including the number of calories it contains, and its benefits and risks to health.

My work is driven by a belief that as AI becomes an even more integral part of our world, it’s imperative to build systems that are transparent, trustworthy, and beneficial. I’m honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity. Chatbots and conversational AI have enormous potential to transform healthcare delivery. As a healthcare leader, you may be wondering about the top use cases for implementing chatbots and how they can benefit your organization specifically.

Learning from experience

Once the primary purpose is defined, common quality indicators to consider are the success rate of a given action, nonresponse rate, comprehension quality, response accuracy, retention or adoption rates, engagement, and satisfaction level. The ultimate goal is to assess whether chatbots positively affect and address the 3 aims of health care. Regular quality checks are especially critical for chatbots acting as decision aids because they can have a major impact on patients’ health outcomes. Health care data are highly sensitive because of the risk of stigmatization and discrimination if the information is wrongfully disclosed. The ability of chatbots to ensure privacy is especially important, as vast amounts of personal and medical information are often collected without users being aware, including voice recognition and geographical tracking. The public’s lack of confidence is not surprising, given the increased frequency and magnitude of high-profile security breaches and inappropriate use of data [95].

The number of studies assessing the development, implementation, and effectiveness are still relatively limited compared with the diversity of chatbots currently available. Further studies are required to establish the efficacy across various conditions and populations. Nonetheless, chatbots for self-diagnosis are an effective way of advising patients as the first point of contact if accuracy and sensitivity requirements can be satisfied. We identified 6 broad use-case categories and 15 use cases where chatbots were deployed in the Covid-19 public health response. Chatbots are scalable, enable social distancing, augment the capacity of healthcare and public health workers, broadly disseminate information, and gather real-time information from a broad audience to inform public health interventions.

To test and evaluate the accuracy and completeness of GPT-4 as compared to GPT-3.5, researchers asked both systems 44 questions regarding melanoma and immunotherapy guidelines. The mean score for accuracy improved from 5.2 to 5.7, while the mean score for completeness improved from 2.6 to 2.8, as medians for both systems were 6.0 and 3.0, respectively. 1The MVP is not dead and here is why2The main steps of MVP development3Best practices for creating an MVP4Summing up Say, you have this amazing idea for a software product but you are not too sure about whether it’s going to be a success or not. As well, virtual nurses can send daily reminders about the medicine intake, ask patients about their overall well-being, and add new information to the patient’s card. In this way, a patient does not need to directly contact a doctor for an advice and gains more control over their treatment and well-being.

The dominos fall when chatbots push patients from traditional clinical face-to-face practice to more complicated automated systems. One of the key elements of expertise and its recognition is that patients and others can trust the opinions and decisions offered by the expert/professional. However, in the case of chatbots, ‘the most important factor for explaining trust’ (Nordheim et al. 2019, p. 24) seems to be expertise.

Hacking (1975) has reminded us of the dual nature between statistical probability and epistemic probability. Statistical probability is concerned with ‘stochastic laws of chance processes’, while epistemic probability gauges ‘reasonable degrees of belief in propositions quite devoid of statistical background’ (p. 12). Epistemic probability concerns our possession of knowledge, or information, meaning how much support is given by all the available evidence. Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes.

As the AI field lacks diversity, bias at the level of the algorithm and modeling choices may be overlooked by developers [102]. In a study using 2 cases, differences in prediction accuracy were shown concerning gender and insurance type for intensive care unit mortality and psychiatric readmissions [103]. On a larger scale, this may exacerbate barriers to health care for minorities or underprivileged individuals, leading to worse health outcomes. Identifying the source of algorithm bias is crucial for addressing health care disparities between various demographic groups and improving data collection. With the vast number of algorithms, tools, and platforms available, understanding the different types and end purposes of these chatbots will assist developers in choosing the optimal tools when designing them to fit the specific needs of users.