Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review PMC

For healthcare businesses, the adoption of chatbots may become a strategic advantage. Discover what they are in healthcare and their game-changing potential for business. They simulate human activities, helping people search for information and perform actions, which many healthcare organizations find useful.

Pasquale (2020, p. 57) has reminded us that AI-driven systems, including chatbots, mirror the successes and failures of clinicians. However, machines do not have the human capabilities of prudence and practical wisdom or the flexible, interpretive capacity to correct mistakes and wrong decisions. As a result of self-diagnosis, physicians may have difficulty convincing patients of their potential preliminary, chatbot-derived misdiagnosis. This level of persuasion and negotiation increases the workload of professionals and creates new tensions between patients and physicians.

The Pros and Cons of Chatbots in Healthcare

Eligible interventions were chatbots operating as standalone software or via a web browser. Chatbots that were integrated into robotics, serious games, SMS, or telephone systems were excluded. The current review also excluded chatbots that relied on human-operator generated dialogue.

• ChatGPT is a large language model using vast amounts of data to generate predictive text responses to user queries.
• Though previously used mainly as virtual assistants and in customer service, ChatGPT has ignited our fascination with the potential of chatbots to change the world.
• Third, it can perform an ‘assessment of a sickness or its risks’ and guide ‘the resident to receive treatment in services promoting health and well-being within Omaolo and in social and health services external to’ it (THL 2020, p. 14).
• This review showed that there is a lack of evidence assessing the effectiveness and safety of chatbots.

Risk of bias graph for randomized controlled trials, showing the review authors’ judgments about each risk of bias item. RNumbers do not add up as two studies assessed outcomes at 2 different points of time. Of the remaining 83 studies, 9 studies were included after reading the full text. Two additional studies were identified from forward reference list checking, and one study was identified by backward reference list checking.

Moral and Ethical Constraints

A recent study published in the Journal of the American Medical Informatics Association found that chatbots in healthcare are deemed most helpful when the chatbot’s ability, patient compliance, integrity and benevolence match that of a human agent. System developers should consider implementing more chatbots in developing countries. The search sources were 7 bibliographic databases (eg, MEDLINE, EMBASE, PsycINFO), the search engine “Google Scholar,” and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently selected the studies, extracted data from the included studies, and assessed the risk of bias. Data extracted from studies were synthesized using narrative and statistical methods, as appropriate.

The study focused on health-related apps that had an embedded text-based conversational agent and were available for free public download through the Google Play or Apple iOS store, and available in English. A healthbot was defined as a health-related conversational agent that facilitated a bidirectional (two-way) conversation. Applications that only sent in-app text reminders and did not receive any text input from the user were excluded.

Chatbot Keeps Your Patients Satisfied

It simplifies the process and speed of diagnosis, as patients no longer need to visit the clinic and communicate with doctors on every request. They only must install the necessary sensors and an application to perform the required tasks. As a result, the clinic staff can quickly access patients’ vital signs and health status. Aside from connecting to patient management systems, the chatbot requires access to a database of responses, which it can pull and provide to patients. Companies limit their potential if they invest in an AI chatbot capable of drawing data from only a few apps. AI conversational chatbots can also utilize more complex tasks, such as changing treatment plans based on a patient’s health status, such as adding a new medication if a patient is found to be allergic to a medication prescribed by a provider.

In addition, health chatbots have been deemed promising in terms of consulting patients in need of psychotherapy once COVID-19-related physical distancing measures have been lifted. Artificial intelligence (AI) is at the forefront of transforming numerous aspects of our lives by modifying the way we analyze information and improving decision-making through problem solving, reasoning, and learning. Machine learning (ML) is a subset of AI that improves its performance based on the data provided to a generic algorithm from experience rather than defining rules in traditional approaches [1]. Advancements in ML have provided benefits in terms of accuracy, decision-making, quick processing, cost-effectiveness, and handling of complex data [2]. Chatbots, also known as chatter robots, smart bots, conversational agents, digital assistants, or intellectual agents, are prime examples of AI systems that have evolved from ML. The Oxford dictionary defines a chatbot as “a computer program that can hold a conversation with a person, usually over the internet.” They can also be physical entities designed to socially interact with humans or other robots.

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Youper monitors patients’ mental states as they chat about their emotional well-being and swiftly starts psychological techniques-based, tailored talks to improve patients’ health. The AI-enabled chatbot can analyze patients’ symptoms according to certain parameters and provide chatbot in healthcare information about possible conditions, diagnoses, and medications. Sometimes a chatbot can even catch what a human doctor misses, especially when looking for patterns in many cases. Part of the responsibility for the ineffectiveness of medical care lies with patients.

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. Thus, interoperability on multiple common platforms is essential for adoption by various types of users across different age groups. In addition, voice and image recognition should also be considered, as most chatbots are still text based. Further refinements and large-scale implementations are still required to determine the benefits across different populations and sectors in health care [26]. Although overall satisfaction is found to be relatively high, there is still room for improvement by taking into account user feedback tailored to the patient’s changing needs during recovery.

However, we used only post-intervention data in each group for the meta-analysis because studies did not report enough data (eg, change in SD or SE of the mean between the pre-intervention and post-intervention for each group). In this review, it was possible to meta-analyze pre-intervention and post-intervention data from one-group trials (ie, did not include comparison groups). However, such analysis was not carried out in this review as such trials are very vulnerable to several threats of internal validity, such as maturation threat, instrumentation threat, regression threat, and history threat [41,48].

Weak evidence demonstrated that chatbots were effective in improving depression, distress, stress, and acrophobia. In contrast, according to similar evidence, there was no statistically significant effect of using chatbots on subjective psychological wellbeing. Results were conflicting regarding the effect of chatbots on the severity of anxiety and positive and negative affect. Only two studies assessed the safety of chatbots and concluded that they are safe in mental health, as no adverse events or harms were reported.

Quality of information and appropriateness of ChatGPT outputs for urology patients

Unfortunately, according to a study in the journal Evidence Based Mental Health, the true clinical value of most apps was ‘impossible to determine’. To develop social bots, designers leverage the abundance of human–human social media conversations that model, analyse and generate utterances through NLP modules. However, the use of therapy chatbots among vulnerable patients with mental health problems bring many sensitive ethical issues to the fore. Chatbots experience the Black

Box problem, which is similar to many computing systems programmed using ML that are trained on massive data sets to produce multiple layers of connections.