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

Providing answers to policyholders is a leading insurance chatbot use case. Bots can be fed with the information on companies’ insurance policies as common issues and integrate the same with an insurance knowledge base. Keeping operational costs low is crucial for any business, and insurance companies are no different.

• The chatbot can send the client proactive information about account updates, and payment amounts and dates.
• Healthcare chatbots can remind patients about the need for certain vaccinations.
• Many people who make an appointment for a colonoscopy, for example, cancel it or fail to show up.
• Data that is enabled for being distributed through bots can be sent as required, any time.

Most of the time, the relationship between healthcare facilities and patients is very passive. Herbie can answer general questions and respond appropriately in a human voice anywhere and at any time. With that being said, we could end up seeing AI chatbots helping with diagnosing illnesses or prescribing medication. We would first have to master how to ethically train chatbots to interact with patients about sensitive information and provide the best possible medical services without human intervention.

Best Tools for Creating Insurance Chatbots

This would save physical resources, manpower, money and effort while accomplishing screening efficiently. The chatbots can make recommendations for care options once the users enter their symptoms. Maya assists users in completing the forms necessary for obtaining a quote for an insurance policy.

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. Chatbots are now able to provide patients with treatment and medication information after diagnosis without having to directly contact a physician. Such a system was proposed by Mathew et al [30] that identifies the symptoms, predicts the disease using a symptom–disease data set, and recommends a suitable treatment. Although this may seem as an attractive option for patients looking for a fast solution, computers are still prone to errors, and bypassing professional inspection may be an area of concern.

Sensely Virtual Assistant

It also helps doctors save time and attend to more patients by answering people’s most frequently asked questions and performing repetitive tasks. Many insurers see chatbots as an opportunity for a new approach to customer service, as well as streamlining the purchase and claims processes. According to a 2019 LexisNexis survey, more than 80% of large U.S. insurers have fully deployed AI solutions in place including the research and development of chatbots.

Use this insurance chatbot template wherein you can engage your customers in an interactive way and at the same time fetch their data by creating a better customer experience. Furthermore, a chatbot can offer complete guidance to patients and it can even solve their queries related to filling insurance claims. It can eventually support them in getting claims faster in the healthcare sector.

Healthcare chatbots significantly cut unnecessary spending by allowing patients to perform minor treatments or procedures without visiting the doctor. To accelerate care delivery, a chatbot can collect required patient data (e.g., address, symptoms, insurance details) and keep this information in EHR. To develop an AI-powered healthcare chatbot, ScienceSoft’s software architects usually use the following core architecture and adjust it to the specifics of each project. A chatbot helps in providing accurate information about COVID-19 in different languages. And, AI-driven chatbots help to make the screening process fast and efficient. And user privacy is a vital problem when it comes to any kind of AI application and sharing data regarding a patient’s medical condition with a chatbot appears less trustworthy than sharing the same data with a human.

This means that the systems’ behavior is hard to explain by merely looking inside, and understanding exactly how they are programmed is nearly impossible. 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].

We’ve analyzed 4 million chatbot conversations. Here’s what we found out.

Thanks to advances in machine learning, the chatbot can answer not only simple questions but also more complex ones. According to a report from Accenture, over 40% of healthcare executives consider AI the technology that will have the greatest impact on their organizations within the next three years. Healthcare providers are already using various types of artificial intelligence, such as predictive analytics or machine learning, to address various issues.

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].

Integration with existing systems and workflows

Training sessions can often be boring, for both new and experienced professionals. These bots can explain things, give quizzes, and show different situations to help trainees learn better. Trainees can also talk to these bots to learn about different types of insurance, how policies work, and the steps for relevant topics. At this stage, the insurance company pays the insurance amount to the policyholder. The chatbot can send the client proactive information about account updates, and payment amounts and dates. Claim filing or First Notice of Loss (FNOL) requires the policyholder to fill a form and attach documents.

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