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

But if the issue is serious, a chatbot can transfer the case to a human representative through human handover, so that they can quickly schedule an appointment. Use cases for healthcare chatbots vary from diagnosis and mental health support to more routine tasks like scheduling and medication reminders. In a world where an anxiety attack can happen at any time, you can rest easy knowing that you have AI-powered chatbots in healthcare to rely on. Patients can easily book, reschedule, or cancel appointments through a simple, conversational interface. This convenience reduces the administrative load on healthcare staff and minimizes the likelihood of missed appointments, enhancing the efficiency of healthcare delivery.

The other obstacle to have a holistic overview of a patient’s circumstance is the lack of a unified and semantic-based approach for publishing and integrating an individual patient’s data. This gap hinders the health care system to provide a comprehensive history and insight about patients. To tackle this deficiency, we propose to publish a knowledge graph out of anonymized patient data that is collected from various sources (knowledge collected from EMR, IoTs devices, and external web services).

Chatbot Keeps Your Patients Satisfied

They have the potential to prevent misinformation, detect symptoms, and lessen the mental health burden during global pandemics [111]. At the global health level, chatbots have emerged as a socially responsible technology to provide equal access to quality health care and break down the barriers between the rich and poor [112]. To further advance medicine and knowledge, the use of chatbots in education for learning and assessments is crucial for providing objective feedback, personalized content, and cost-effective evaluations [113]. For example, the development of the Einstein app as a web-based physics teacher enables interactive learning and evaluations but is still far from being perfect [114]. Given chatbots’ diverse applications in numerous aspects of health care, further research and interdisciplinary collaboration to advance this technology could revolutionize the practice of medicine.

For example, if your patient is using the medication reminder already, you can add a symptom check for each of the reminders. So, for diabetic treatment, the chatbot can ask if the patient had any symptoms during the day. And for pain medication, the bot can display a pain level scale and ask how much pain the patient is in at the moment of fulfilling the survey. A patient can open the chat window and self-schedule a visit with their doctor using a bot. Just remember that the chatbot needs to be connected to your calendar to give the right dates and times for appointments.

Recommended health care components for the different types of chatbots.

However, the details of experiencing chatbots and their expertise as trustworthy are a complex matter. As Nordheim et al. have pointed out, ‘the answers not only have to be correct, but they also need to adequately fulfil the users’ needs and expectations for a good answer’ (p. 25). Importantly, in addition to human-like answers, the perceived human-likeness of chatbots in general can be considered ‘as a likely predictor of users’ trust in chatbots’ (p. 25).

Furthermore, since you can integrate the bot with your internal hospital system, the bot can seamlessly transfer the data into it. It saves you the hassle of manually adding data and keeping physical copies that you fetch whenever there’s a returning patient. As a foundational pillar of modern society, healthcare is probably one of the most important industries there is today. With each answer you give the chatbot, it eliminates a couple of diagnosis options until it finally lands on the most likely ones.

In the case of Tessa, a wellness chatbot provided harmful recommendations due to errors in the development stage and poor training data. Chatbots in the healthcare industry provide support by recommending coping strategies for various mental health problems. A healthcare chatbot can also help patients with health insurance claims and billing—something that can often be a source of frustration and confusion for healthcare consumers.

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No studies have been found to assess the effectiveness of chatbots for smoking cessation in terms of ethnic, racial, geographic, or socioeconomic status differences. Creating chatbots with prespecified answers is simple; however, the problem becomes more complex when answers are open. Bella, one of the most advanced text-based chatbots on the market advertised as a coach for adults, gets stuck when responses are not prompted [51]. Given all the uncertainties, chatbots hold potential for those looking to quit smoking, as they prove to be more acceptable for users when dealing with stigmatized health issues compared with general practitioners [7]. Early cancer detection can lead to higher survival rates and improved quality of life. Inherited factors are present in 5% to 10% of cancers, including breast, colorectal, prostate, and rare tumor syndromes [62].

It also helps patients stay posted about their upcoming healthcare appointments and medication schedule so they don’t miss any doses or get confused about what they need to take when they go into the doctor’s office again. A healthcare assistant bot interacts with the patient via various conversational interfaces (voice, text, and visual) to disseminate information and provide recommendation (validated by physician). The core functionalities of the chatbot (Component C in the blue box) are extended with a background HKG (Component A in the green box) and an evolving PHKG (Component B in the orange box). Appointment scheduling via a chatbot significantly reduces the waiting times and improves the patient experience, so much so that 78% of surveyed physicians see it as a chatbot’s most innovative and useful application.

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]. Unlike financial data that becomes obsolete after being stolen, medical data are particularly valuable, as they are not perishable. Privacy threats may break the trust that is essential to the therapeutic physician–patient relationship and inhibit open communication of relevant clinical information for proper diagnosis and treatment [96]. In terms of cancer diagnostics, AI-based computer vision is a function often used in chatbots that can recognize subtle patterns from images. This would increase physicians’ confidence when identifying cancer types, as even highly trained individuals may not always agree on the diagnosis [52]. Studies have shown that the interpretation of medical images for the diagnosis of tumors performs equally well or better with AI compared with experts [53-56].

The proven chatbot use cases we have explored demonstrate the significant impact these AI-driven tools can have on businesses and organizations. From enhancing customer service to optimizing sales and streamlining various processes, chatbots have shown their ability to deliver efficient and personalized results. As technology continues to evolve and businesses recognize the value of chatbots, their popularity is predicted to rise even further. Gartner predicts that by 2027, approximately 25% of organizations will have chatbots as their main customer service channel. With their increasing adoption and advancements in AI technologies, chatbots are poised to play an even more critical role in shaping the future of customer engagement and service delivery.

In addition, automated diagnosis may be useful when there are not enough specialists to review the images. 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 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].

In simple terms, conversational AI is a category of AI-driven solutions that automate human-like conversations with users. It utilizes techniques like natural language processing and machine learning to tap into their learnings and deliver clear answers to varied questions in a conversational tone. This can help the facility avoid cases where bills were sent to patients with no coverage. A chatbot can also help a healthcare facility determine what types of insurance plans they accept and how much they will reimburse for specific services or procedures.

Marketing Chatbot Use Cases

Hence, it’s very likely to persist and prosper in the future of the healthcare industry. Today’s healthcare chatbots are obviously far more reliable, effective, and interactive. As advancements in AI are ever evolving and ameliorating, chatbots will inevitably perform a range of complex activities and become an indispensable part of many industries, mainly, healthcare. The idea of a digital personal assistant is tempting, but a healthcare chatbot goes a mile beyond that. From patient care to intelligent use of finances, its benefits are wide-ranging and make it a top priority in the Healthcare industry. Healthcare chatbots enable you to turn all these ideas into a reality by acting as AI-enabled digital assistants.

• By sending timely reminders and tracking medication schedules, they ensure that patients follow their prescribed treatments effectively.
• Insufficient consideration regarding the implementation of chatbots in health care can lead to poor professional practices, creating long-term side effects and harm for professionals and their patients.
• Many healthcare service providers are transforming FAQs by incorporating an interactive healthcare chatbot to respond to users’ general questions.
• Therefore, while utilizing official sources is a prudent practice, especially for off-the-shelf solutions and for non-healthcare organizations, more work is required to understand best practices.
• By ensuring that patients attend their appointments and adhere to their treatment plans, these reminders help enhance the effectiveness of healthcare.
• Then you’ll be interested in the fact that chatbots can help you reduce cart abandonment, delight your shoppers with product recommendations, and generate more leads for your marketing campaigns.

The latter aspect could explain why cancer is slowly becoming a chronic disease that is manageable over time [19]. Added life expectancy poses new challenges for both patients and the health care team. For example, many patients now require extended at-home support and monitoring, whereas health care workers deal with an increased workload. Although clinicians’ knowledge base in the use of scientific evidence to guide decision-making has expanded, there are still many other facets to the quality of care that has yet to catch up. Key areas of focus are safety, effectiveness, timeliness, efficiency, equitability, and patient-centered care [20]. This review article aims to report on the recent advances and current trends in chatbot technology in medicine.

We acknowledge the difficulty in identifying the nature of systemic change and looking at its complex network-like structure in the functioning of health organisations. Nonetheless, we consider it important to raise this point when talking about chatbots and their potential breakthrough in health care. We suggest that new ethico-political approaches are required in professional ethics because chatbots can become entangled with clinical practices in complex ways. It is difficult to assess the legitimacy of particular applications and their underlying business interests using concepts drawn from universal AI ethics or traditional professional ethics inherited from bioethics.

In addition, the development of algorithmic systems for health services requires a great deal of human resources, for instance, experts of data analytics whose work also needs to be publicly funded. A complete system also requires a ‘back-up system’ or practices that imply increased costs chatbot use cases in healthcare and the emergence of new problems. The crucial question that policy-makers are faced with is what kind of health services can be automated and translated into machine readable form. Chatbots have the potential to address many of the current concerns regarding cancer care mentioned above.

Chatbots in healthcare can also be used to provide consumers with basic diagnostic assistance and as a tool to assess symptoms before an in-person appointment. It assesses the current emotional state of the user by asking questions, then suggests activities and exercises for them to do. Woebot is transparent about how it cannot replace an appointment with a real human being, but it offers a listening ear and advice, often giving users further information and resources on techniques to better manage their emotions over time. Chatbots and conversational AI have been widely implemented in the mental health field as a cheaper and more accessible option for healthcare consumers. Healthcare chatbots can help medical professionals to better communicate with their patients.