Chatbots: The Future of Healthcare
In addition to the concern of accuracy and validity, addressing clinical utility and effectiveness of improving patients’ quality of life is just as important. With the increased use of diagnostic chatbots, the risk of overconfidence and overtreatment may cause more harm than benefit [99]. There is still clear potential for improved decision-making, as diagnostic deep learning algorithms were found to be equivalent to health care professionals in classifying diseases in terms of accuracy [106]. These issues presented above all raise the question of who is legally liable for medical errors. Avoiding responsibility becomes easier when numerous individuals are involved at multiple stages, from development to clinical applications [107]. Although the law has been lagging and litigation is still a gray area, determining legal liability becomes increasingly pressing as chatbots become more accessible in health care.
Chatbots also support doctors in managing charges and the pre-authorization process. Today’s healthcare chatbots are obviously far more reliable, effective, and interactive. As advancements in AI are ever evolving and ameliorating, chatbots will inevitably chatbot in healthcare perform a range of complex activities and become an indispensable part of many industries, mainly, healthcare. Everyone wants a safe outlet to express their innermost fears and troubles and Woebot provides just that—a mental health ally.
Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review
The effect of using chatbots on positive and negative affect, which is an indicator of depression and anxiety, was examined in 2 studies [28,29]. The outcome in the 3 studies was measured using the Positive and Negative Affect Schedule. Meta-analysis could not be executed as only 1 study reported enough data for the analysis [28]. This study aimed to assess the effectiveness and safety of using chatbots to improve mental health through summarizing and pooling the results of previous studies.
An area of concern is that chatbots are not covered under the Health Insurance Portability and Accountability Act; therefore, users’ data may be unknowingly sold, traded, and marketed by companies [110]. On the other hand, overregulation may diminish the value of chatbots and decrease the freedom for innovators. Consequently, balancing these opposing aspects is essential to promote benefits and reduce harm to the health care system and society. Hesitancy from physicians and poor adoption by patients is a major barrier to overcome, which could be explained by many of the factors discussed in this section. A cross-sectional web-based survey of 100 practicing physicians gathered the perceptions of chatbots in health care [6].
Chatbots: The Future of Healthcare
Now, let’s explore the main applications of artificial intelligence chatbots in healthcare in more detail. Healthcare chatbots are AI-enabled digital assistants that allow patients to assess their health and get reliable results anywhere, anytime. It manages appointment scheduling and rescheduling while gently reminding patients of their upcoming visits to the doctor. It saves time and money by allowing patients to perform many activities like submitting documents, making appointments, self-diagnosis, etc., online. The healthcare industry incorporates chatbots in its ecosystem to streamline communication between patients and healthcare professionals, prevent unnecessary expenses and offer a smooth, around-the-clock helping station. Further, in order to ensure the responsible and effective use of the novel and still-developing technology, ethical concerns and data privacy must be thoroughly addressed.
A bevy of different chatbots are now available, the most prominent of which are ChatGPT and Bard, which is marketed by Google. Both chatbots can produce computer-generated texts that display uncannily humanlike research and writing capabilities. 82% of healthcare consumers who sought pricing information said costs influenced their healthcare decision-making process. 60% of healthcare consumers requested out-of-pocket costs from providers ahead of care, but barely half were able to get the information. Use case for chatbots in oncology, with examples of current specific applications or proposed designs. All authors contributed to the assessment of the apps, and to writing of the manuscript.
However, there is no machine substitute for higher-level interactions, critical thinking, and ambiguity [93]. Chatbots create added complexity that must be identified, addressed, and mitigated before their universal adoption in health care. As such, there are concerns about how chatbots collect, store, and use patient data. Healthcare providers must ensure that privacy laws and ethical standards handle patient data. These simple rule-based chatbots provide patients with helpful information and support using “if-then” logic for conversational flows. Before answering, the bot compares the entered text with pre-programmed responses and displays it to the user if it finds a match; otherwise, it shares a generic fallback answer.
