Pros and cons of conversational AI in healthcare

In 1956, John McCarthy organized the Dartmouth Conference, where he coined the term “Artificial Intelligence.“ This event marked the beginning of the modern AI era. Whether care is happening remotely or in person, AI tools can also streamline revenue cycle management for providers. RPM solutions enable continuous and intermittent recording and transmission of these data.

The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. These impacts are just the beginning of how AI is poised to transform the healthcare industry, and many more changes are likely to emerge as these technologies advance to improve care delivery and patient outcomes. Revenue cycle management is crucial to ensuring that health systems can focus on providing high-quality care for patients. However, effectively tackling revenue challenges and optimizing operations requires heavy lifting on the administrative side. Common RPM tools that take advantage of advanced analytics approaches like AI play a significant role in advancing hospital-at-home programs.

Top 12 ways artificial intelligence will impact healthcare

Receiving personalised support from health professionals, such as general practitioners/physicians, dietitians and exercise physiologists, is one of the most effective interventions to improve these behaviours. However, interaction with health professionals often requires traditional on-site (in-person) visits, and substantial time, travel and financial costs for patients12. Furthermore, these services are often limited to specific patient populations, such as those with a diagnosed chronic disease. As such, many individuals with poor health behaviours (who are at increased risk of chronic disease), may have limited access to support from health professionals to modify their lifestyle and reduce disease risk in the future.

To address this limitation, we applied population weightings in regression models based on respective regions’ census data to adjust for potential biases. Third, the chatbot employed in Hong Kong and Singapore only had COVID-19 vaccine-related content and was unable to answer general COVID-19 questions (i.e., COVID-19 home care instructions, daily COVID-19 cases). As a result, participants might have engaged less with the chatbot and rated the chatbots as less helpful than they would have otherwise. Fourth, our study might have social desirability bias since outcomes are self-reported amid active governmental encouragement and mandates on vaccination during the Omicron outbreak. Fifth, our study design incorporated responses of guardians to gauge vaccine confidence and acceptance of unvaccinated seniors due to lacking eligible senior participants in the existing panel.

The good thing is that you can train an AI chatbot to reduce the possibility of these risks. My company often works with clients who want to use customized AI tools, including chatbots. There are several development practices that help us make sure we deliver solutions that drive their businesses forward. The article further mentioned there had been unethical use of chat interfaced LLMs in ‚experiments‘ on patients without consent.

Literature review and hypothesis development

Accuracy metrics are scored based on domain and task types, trustworthiness metrics are evaluated according to the user type, empathy metrics consider patients needs in evaluation (among the user type), and performance metrics are evaluated based on the three confounding variables. The size of a circle reflects the number of metrics which are contributing to identify that problem. However, they solely rely on surface-form similarity and language-specific perspectives, rendering them benefits of chatbots in healthcare inadequate for healthcare chatbots. You can foun additiona information about ai customer service and artificial intelligence and NLP. These metrics lack the capability to capture essential elements such as semantics19,20, context19,21, distant dependencies22,23, semantically critical ordering change21, and human perspectives, particularly in real-world scenarios. The key findings of our study are that chatbot interventions targeting physical activity, fruit and vegetable consumption, sleep duration, and sleep quality show significant effects in improving these outcomes.

Medical (social) chatbots can interact with patients who are prone to anxiety, depression and loneliness, allowing them to share their emotional issues without fear of being judged, and providing good advice as well as simple company. A study by Smith and Anderson (2018) found that individuals are more likely to discuss sensitive issues when they feel their identity is protected. This can be particularly important for vulnerable populations who may be hesitant to seek help from human therapists due to social or cultural stigma. To learn more about the ways AI can support the work of healthcare professionals and staff, check out our discussion paper on how smart automation is easing administrative burden in medicine.

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The error type “superficial” was assigned 28 times for ChatGPT-3.5 and 16 times for ChatGPT-4. In ChatGPT-3.5, four sentences, and in ChatGPT-4, two sentences did not distinguish between evidence and recommendation (Fig. 1). After the initial independent rating by the two raters (SB and MK), who are physicians specialised in intensive care medicine and teaching in resuscitation including ERC courses, discordant ratings were discussed, and a consensus was reached for the final rating.

By enabling healthcare services to transcend geographical barriers, chatbots empower patients with unparalleled access to care while relieving the strain on overburdened healthcare facilities (8). The landscape of healthcare communication is undergoing a profound transformation in the digital age, and at the heart of this evolution are AI-powered chatbots. This mini-review ChatGPT App delves into the role of AI chatbots in digital health, providing a detailed exploration of their applications, benefits, challenges, and future prospects. Our focus is on their versatile applications within healthcare, encompassing health information dissemination, appointment scheduling, medication management, remote patient monitoring, and emotional support services.

AI is changing not just how patients interact with bots but also how doctors go about their tasks. Chatbots, like AWS HealthScribe, can recognize speaker roles, categorize dialogues, and identify medical terminology to create initial clinical documentation, Ryan Gross, head of data and applications at Caylent, told PYMNTS. This technology streamlines the data collection and documentation process, freeing healthcare professionals to focus on patient care. Lawless mentioned that chatbots can quickly help simplify medical information and treatment plans, making things more explicit for patients and serving a wide range of people. Often, physicians provide detailed explanations and support when patients might not be best positioned to absorb the information, such as immediately following a procedure.

Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being

A helpful comparison to reiterate the collaborative nature needed between AI and humans for healthcare is that in most cases, a human pilot is still needed to fly a plane. Although technology has enabled quite a bit of automation in flying today, people are needed to make adjustments, interpret the equipment’s data, and take over in cases of emergency. AI also can help promote information on disease prevention online, reaching large numbers of people quickly, and even analyze text on social media to predict outbreaks. Considering the example of a widespread public health crisis, think of how these examples might have supported people during the early stages of COVID-19. For example, a study found that internet searches for terms related to COVID-19 were correlated with actual COVID-19 cases. Here, AI could have been used to predict where an outbreak would happen, and then help officials know how to best communicate and make decisions to help stop the spread.

For instance, “pneumonia is hazardous“ might be challenging for a general audience, while “lung disease is dangerous“ could be a more accessible option for people with diverse health knowledge. Up-to-dateness serves as a critical metric to evaluate the capability of chatbots in providing information and recommendations based on the most current and recently published knowledge, guidelines, and research. Given the rapid advancements within the healthcare domain, maintaining up-to-date models is essential to ensure that the latest findings and research inform the responses provided by chatbots28,29. Up-to-dateness significantly enhances the validity of a chatbot by ensuring that its information aligns with the latest evidence and guidelines. Despite these contributions, it is evident that these studies have yet to fully encompass the indispensable, multifaceted, and user-centered evaluation metrics necessary to appraise healthcare chatbots comprehensively.

While we continue to work against bias in healthcare, AI is being used to triage medical cases by bumping those considered most critical to the top of the care chain. To that end, Cleveland Clinic has become a founding member of a global effort to create an AI Alliance — an international community of researchers, developers and organizational leaders all working together to develop, achieve and advance the safe and responsible use of AI. The AI Alliance, started by IBM and Meta, now includes over 90 leading AI technology and research organizations to support and accelerate open, safe and trusted generative AI research and development.

These initiatives allow patients to receive care outside the hospital setting, necessitating that clinical decision-making must rely on real-time patient data. Predictive analytics enables improved clinical decision support, population health management, and value-based care delivery, and its healthcare applications are continually expanding. AI-driven patient engagement can also take the form of solutions designed to conduct ChatGPT patient outreach based on clinical risk assessment data or tools to translate health information for users in a patient portal. While digital technologies cannot replace the human elements of the patient experience, they have their place in healthcare consumerism. Health data extraction solutions can help clinicians find the information they’re looking for quickly and effectively, reducing information overload.

Although the algorithm effectively predicted the clots, it didn’t improve patient outcomes compared to standard care. Another recent study published in the journal Radiology found that radiologists who used an AI assistant to screen mammograms for signs of cancer were likely to defer to the algorithm’s judgment despite some of the radiologists being highly experienced. This often swayed them to make the wrong diagnosis, lowering their accuracy from 80 percent to 45 percent for highly experienced radiologists (less experienced practitioners performed even worse) because they assumed the AI spotted something they hadn’t. Additionally, a 2021 review of studies showed that patients’ perceptions and opinions of chatbots for mental health are generally positive. The review, which assessed 37 unique studies, pinpointed ten themes in patient perception of mental health chatbots, including usefulness, ease of use, responsiveness, trustworthiness, and enjoyability. The survey polled 65 doctors, therapists, or practice owners/founders in March 2023 who use live chatbots on their websites.

Overcoming The Challenges Of Using AI Chatbots In Healthcare

AI-driven predictive analytics can enhance the accuracy, efficiency, and cost-effectiveness of disease diagnosis and clinical laboratory testing. Additionally, AI can aid in population health management and guideline establishment, providing real-time, accurate information and optimizing medication choices. Integrating AI in virtual health and mental health support has shown promise in improving patient care. However, it is important to address limitations such as bias and lack of personalization to ensure equitable and effective use of AI.

The healthcare chatbot market is expected to flourish at a CAGR of 23.9% from 2024 to 2034. Demand for healthcare chatbot software is projected to remain prominent, accumulating a market share of 62% in 2024. The table further details that cloud-based deployment is expected to account for 63.6% of all deployment types in 2024. The global healthcare chatbot market insights scope rose at a 21.2% CAGR between 2019 and 2023. The healthcare chatbot market is anticipated to develop at a CAGR of 23.9% over the forecast period from 2024 to 2034. A pilot study by academics at the University of Oxford found some care providers had been using generative AI chatbots such as ChatGPT and Bard to create care plans for people receiving care.

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Researchers claim that these technologies enhance decision-making, maximize creativity, increase the effectiveness of research and clinical trials, and produce new tools that benefit healthcare providers, patients, insurers, and regulators [78]. AI enables quick and comprehensive retrieval of drug-related information from different resources through its ability to analyze the current medical literature, drug databases, and clinical guidelines to provide accurate and evidence-based decisions for healthcare providers. Using automated response systems, AI-powered virtual assistants can handle common questions and provide detailed medical information to healthcare providers [79].

Ada quickly gained popularity for its ability to offer personalized recommendations, reassuring users about mild symptoms and highlighting the need for immediate medical attention in severe cases. This chatbot acts as an early triage system, offering insights that contribute to more informed decisions regarding seeking medical help. Another reason for the lack of awareness around healthcare chatbots is the limited marketing and promotion efforts undertaken by their developers. Unlike heavily funded, large-scale tech companies, many healthcare chatbot creators operate on limited budgets, hindering their ability to reach a wide audience.

Doctors can access that information all in one place and use it to research the disease and the effectiveness of different treatment options, and use that information to inform their practice. Well, depending on the area of focus, medical specialty and what’s needed, AI can be used in a variety of ways to impact and improve patient outcomes. When used together, AI and machine learning can help us be more efficient and effective than ever before. These tools are being used with thousands of datasets to improve our ability to research various diseases and treatment options. These tools are also used behind the scenes, even before patients arrive onsite for care, to improve the patient experience. Remote patient monitoring (RPM) has become more familiar to patients following the COVID-19 pandemic and the resulting rise in telehealth and virtual care.

We recognize “the need for improved methodologies,” he says, to identify biased algorithms and improve them. Safety is always the first concern when submitting software for FDA approval, but the other issue is showing that an algorithm actually works. AI in medicine expert Eric Topol, the founder and director of Scripps Research Translational Institute in La Jolla, California, says there’s a lack of transparency and little public disclosure for the 500 or so already-FDA-approved AI models in use. That’s because they are markedly less complex than large language models or chatbots—though it’s hard to say how, specifically.

• AI enables quick and comprehensive retrieval of drug-related information from different resources through its ability to analyze the current medical literature, drug databases, and clinical guidelines to provide accurate and evidence-based decisions for healthcare providers.
• Research is pivotal for refining ChatGPT and ChatGPT-supported chatbots, optimizing their integration into mental health services, and ensuring they meet the evolving needs of users and healthcare providers alike within ethical framework.
• Sensely’s chatbot, equipped with an avatar, helps users navigate their health insurance benefits and connects them directly with healthcare services.
• Despite the general nature of the inquiries on the key messages of the ERC guideline chapters, the AI was able to maintain focus.

Older patients, those with conservative political views, and those with stronger religious views were less likely to trust AI chatbots like ChatGPT. Excluding these voices from the regulatory process could make it easier for these companies to market chatbots and apps like DermAssist as non-medical devices, even when they are being used in medical settings. There are many bombastic claims about how AI can revolutionize medicine, but researchers warn we need better studies, transparency, and data before we can be sure. McKinney explained over email that a cornerstone of the FDA’s current approach to regulating AI and machine learning-based software involves assessing a risk/benefit profile for each device depending on its use, as well as an evaluation of potential biases.

By comparison, a large majority (72%) of those not familiar with this technology prior to the survey say they would not want this. Black adults (57%) are somewhat less likely than White (65%) and Hispanic (69%) adults to say they would want AI used for skin cancer screening. Experts have raised questions about the accuracy of AI-based skin cancer systems for darker skin tones. Among those who see a problem with bias in health and medicine, larger shares think the use of AI would make this issue better than worse among White (54% vs. 12%, respectively), Hispanic (50% vs. 19%) and English-speaking Asian (58% vs. 15%) adults. Views among Black adults also lean in a more positive than negative direction, but by a smaller margin (40% vs. 25%).