From setting appointment reminders and facilitating document submission to providing round-the-clock patient support, these digital assistants are enhancing the healthcare experience for both providers and patients. Hence, chatbots will continue to help users navigate services about their healthcare. In this regard, chatbots may be in the future will issue reminders, schedule appointments, or help refill prescription medicines.
Patients are often overwhelmed by information in the discharge process, and a chatbot provides them with an avenue of communication that they can use to ask questions about upcoming procedures, recovery exercises, or medication. Chatbots collect patient information, name, birthday, contact information, current doctor, last visit to the clinic, and prescription information. The chatbot submits a request to the patient’s doctor for a final decision and contacts the patient when a refill is available and due.
These data are not intended to quantify the penetration of healthbots globally, but are presented to highlight the broad global reach of such interventions. Another limitation stems from the fact that in-app purchases were not assessed; therefore, this review highlights features and functionality only of apps that are free to use. Lastly, our review is limited by the limitations in reporting on aspects of security, privacy and exact utilization of ML. While our research team assessed the NLP system design for each app by downloading and engaging with the bots, it is possible that certain aspects of the NLP system design were misclassified. There were 47 (31%) apps that were developed for a primary care domain area and 22 (14%) for a mental health domain.
This was made possible through deep learning algorithms in combination with the increasing availability of databases for the tasks of detection, segmentation, and classification [57]. For example, Medical Sieve (IBM Corp) is a chatbot that examines radiological images to aid and communicate with cardiologists and radiologists to identify issues healthcare chatbot use cases quickly and reliably [24]. Similarly, InnerEye (Microsoft Corp) is a computer-assisted image diagnostic chatbot that recognizes cancers and diseases within the eye but does not directly interact with the user like a chatbot [42]. Even with the rapid advancements of AI in cancer imaging, a major issue is the lack of a gold standard [58].
If you are considering chatbots and automation as part of your innovation plan, take time to put together a solid strategy and roadmap. 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. Although they are capable of solving complex problems that are unimaginable by humans, these systems remain highly opaque, and the resulting solutions may be unintuitive.
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For example, Northwell Health recently launched a chatbot to reduce the number of no-shows for the colonoscopy procedure, which is critical for diagnosing colorectal cancer. This issue was particularly concerning because 40% of the less privileged patients didn’t follow through with the procedure. In any case, this AI-powered chatbot is able to analyze symptoms, find potential causes for them, and follow up with the next steps. While the app is overall highly popular, the symptom checker is only a small part of their focus, leaving room for some concern. One of the first healthcare chatbot companies we wanted to talk about is Google’s Med-PaLM 2.
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. They provide preliminary assessments, answer general health queries, and facilitate virtual consultations. This support is especially important in remote areas or for patients who have difficulty accessing traditional healthcare services, making healthcare more inclusive and accessible. By quickly assessing symptoms and medical history, they can prioritize patient cases and guide them to the appropriate level of care.