Beyond Boundaries: The Promise Of Conversational AI In Healthcare

As for healthcare chatbot examples, Kyruus assists users in scheduling appointments with medical professionals. Informative, conversational, and prescriptive healthcare chatbots can be built into messaging services like chatbot technology in healthcare Facebook Messenger, Whatsapp, or Telegram or come as standalone apps. By using this information, a medical organization can analyze the efficiency and quality of their services and identify areas for improvement.

People who suffer from depression, anxiety disorders, or mood disorders can converse with this chatbot, which, in turn, helps people treat themselves by reshaping their behavior and thought patterns. Furthermore, hospitals and private clinics use medical chat bots to triage and clerk patients even before they come into the consulting room. These bots ask relevant questions about the patients’ symptoms, with automated responses that aim to produce a sufficient medical history for the doctor. Subsequently, these patient histories are sent via a messaging interface to the doctor, who triages to determine which patients need to be seen first and which patients require a brief consultation.

An Overview of the Use of Chatbots in Medical and Healthcare Education

For both users and developers, transparency becomes an issue, as they are not able to fully understand the solution or intervene to predictably change the chatbot’s behavior [97]. With the novelty and complexity of chatbots, obtaining valid informed consent where patients can make their own health-related risk and benefit assessments becomes problematic [98]. Without sufficient transparency, deciding how certain decisions are made or how errors may occur reduces the reliability of the diagnostic process. The Black Box problem also poses a concern to patient autonomy by potentially undermining the shared decision-making between physicians and patients [99]. The chatbot’s personalized suggestions are based on algorithms and refined based on the user’s past responses.

Most (19/32, 59%) of the included papers included screenshots of the user interface. In such cases, we marked the chatbot as using a combination of input methods (see Figure 5). Studies that detailed any user-centered design methodology applied to the development of the chatbot were among the minority (3/32, 9%) [16-18].

Types of Chatbots

UK health authorities have recommended apps, such as Woebot, for those suffering from depression and anxiety (Jesus 2019). Pasquale (2020, p. 46) pondered, ironically, that cheap mental health apps are a godsend for health systems pressed by austerity cuts, such as Britain’s National Health Service. Unfortunately, according to a study in the journal Evidence Based Mental Health, the true clinical value of most apps was ‘impossible to determine’.

Promising progress has also been made in using AI for radiotherapy to reduce the workload of radiation staff or identify at-risk patients by collecting outcomes before and after treatment [70]. An ideal chatbot for health care professionals’ use would be able to accurately detect diseases and provide the proper course of recommendations, which are functions currently limited by time and budgetary constraints. Continual algorithm training and updates would be necessary because of the constant improvements in current standards of care. Further refinements and testing for the accuracy of algorithms are required before clinical implementation [71]. This area holds tremendous potential, as an estimated ≥50% of all patients with cancer have used radiotherapy during the course of their treatment. Chatbots are becoming a trend in many fields such as medical, service industry and more recently in education.

How to design a healthcare chatbot using machine learning techniques?

Chatbots are computer programs that present a conversation-like interface through which people can access information and services. The COVID-19 pandemic has driven a substantial increase in the use of chatbots to support and complement traditional health care systems. However, despite the uptake in their use, evidence to support the development and deployment of chatbots in public health remains limited. Recent reviews have focused on the use of chatbots during the COVID-19 pandemic and the use of conversational agents in health care more generally.