Evaluating the accuracy and reliability of AI chatbots in disseminating the content of current resuscitation guidelines: a comparative analysis between the ERC 2021 guidelines and both ChatGPTs 3 5 and 4 Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine Full Text

AI chatbots can help bridge this gap by offering support to those without access to mental health care. AI chatbots are at the confluence between developing technology and ChatGPT App altering healthcare requirements. They envision a future in which receiving medical treatment would be more like a tailored and engaging adventure than a simple service.

Yun and Park (2022), conversely, found that the reliability of chatbot service quality positively impacts users’ satisfaction and repurchase intention. AI has the potential to revolutionize clinical practice, but several challenges must be addressed to realize its full potential. Among these challenges is the lack of quality medical data, which can lead to inaccurate outcomes. Data privacy, availability, and security are also potential limitations to applying AI in clinical practice.

Chatbots aid healthcare providers in triaging patients efficiently, allowing healthcare facilities to allocate resources effectively. The AI-backed algorithms integrated into Chatbots assist in assessing symptoms and providing initial guidance, thereby helping patients determine the necessary next steps in their healthcare journey. This seamless triage process not only reduces the burden on emergency departments but also optimizes patient flow throughout healthcare systems.

Data availability statement

Digital tools like DUOS are trained with documents from Medicare, so it can give personalized responses based on your health needs and budget. Instead of waiting for the next customer service representative, you can use an AI chatbot to answer Medicare benefits questions or help you choose between plans. Since DUOS is trained with updated information from Medicare, you will receive relevant responses about your options or new available benefits.

That question is still up in the air—there haven’t been any court cases that have leveled blame at individual doctors, hospital administrators, companies, or regulators themselves. Several physicians proto.life spoke to admitted that they’ve heard of cases where colleagues are already using tools like ChatGPT in practice. In many cases, the tasks are innocuous—they use it for things like drafting form letters to insurance companies and to otherwise unburden themselves of small and onerous office duties.

In summary, when confronted with irrational factors such as social pressure and intuitive negative cues, people are more likely to reject health chatbots. This is consistent with previous research by Sun et al. (2023), who discovered that the presence of emotional disgust toward smartphone apps reduced individuals’ adoption intentions. This result reaffirms the prior finding that prototype perceptions have a greater influence through behavioral willingness, and thus impact individual behavior (Myklestad and Rise, 2007; Abedini et al., 2014; Elliott et al., 2017). Addressing these challenges and providing constructive solutions will require a multidisciplinary approach, innovative data annotation methods, and the development of more rigorous AI techniques and models. Creating practical, usable, and successfully implemented technology would be possible by ensuring appropriate cooperation between computer scientists and healthcare providers. By merging current best practices for ethical inclusivity, software development, implementation science, and human-computer interaction, the AI community will have the opportunity to create an integrated best practice framework for implementation and maintenance [116].

The Future Role of Healthcare Chatbots

Compounding these issues is the models’ “black box” nature, which obscures the interpretability of their decision-making processes, posing significant hurdles in sectors that mandate transparency and accountability. You can foun additiona information about ai customer service and artificial intelligence and NLP. Addressing these multi-faceted challenges requires a robust approach that balances innovation with the ethical and responsible use of AI. If certain classes are overrepresented or underrepresented, the resultant chatbot model may be skewed towards predicting the overrepresented classes, thereby leading to unfair outcomes for the underrepresented classes (22). One notable algorithm in the field of federated learning is the Hybrid Federated Dual Coordinate Ascent (HyFDCA), proposed in 2022 (14). HyFDCA focuses on solving convex optimization problems within the hybrid federated learning setting. It employs a primal-dual setting, where privacy measures are implemented to ensure the confidentiality of client data.

These intelligent virtual assistants can understand and respond to patient inquiries in real-time, providing accurate and relevant information based on their input. By leveraging vast medical knowledge and continuously learning from patient interactions, AI-powered chatbots offer a revolutionary approach to patient triage in healthcare settings. Also to ensure accuracy, the chatbots are not providing answers just based on what’s appeared on the internet, which is how chatbots most often used by the public (including ChatGPT) are trained.

Collaboration between healthcare organizations, AI researchers, and regulatory bodies is crucial to establishing guidelines and standards for AI algorithms and their use in clinical decision-making. Investment in research and development is also necessary to advance AI technologies tailored to address healthcare challenges. Therapeutic drug monitoring (TDM) is a process used to optimize drug dosing in individual patients. It is predominantly utilized for drugs with a narrow therapeutic index to avoid both underdosing insufficiently medicating as well as toxic levels.

Developing vast language models entails navigating complex ethical, legal, and technical terrains. Such models, while powerful, risk propagating biases from their extensive training datasets, which can lead to skewed outcomes with real-world implications. Legally, they straddle issues of copyright infringement and are capable of generating deepfakes, which presents challenges for content authenticity and intellectual property rights. Moreover, automated content generation faces disparate regulations across borders, complicating global deployment. Artificial Intelligence (AI)-powered chatbots are becoming significant tools in the transformation of healthcare in the 21st century, facilitating the convergence of technology and delivery of medical services. Moreover, model overfitting, where a model learns the training data too well and is unable to generalize to unseen data, can also exacerbate bias (21).

Benefits And Risks Of Using Out-Of-The-Box AI Chatbots

By establishing standardized questions for each metric category and its sub-metrics, evaluators exhibit more uniform scoring behavior, leading to enhanced evaluation outcomes7,34. Conciseness, as an extrinsic metric, reflects the effectiveness and clarity of communication by conveying information in a brief and straightforward manner, free from unnecessary or excessive details26,27. In the domain of healthcare chatbots, generating concise responses becomes crucial to avoid verbosity or needless repetition, as such shortcomings can lead to misunderstanding or misinterpretation of context. Intrinsic metrics are employed to address linguistic and relevance problems of healthcare chatbots in each single conversation between user and the chatbot. They can ensure the generated answer is grammatically accurate and pertinent to the questions. Healthcare organizations may consider patient education about the benefits of AI chatbots in initial disease diagnosis, especially as AI becomes a more important topic in healthcare.

We aim to establish unified benchmarks specifically tailored for evaluating healthcare chatbots based on the proposed metrics. Additionally, we plan to execute a series of case studies across various medical fields, such as mental and physical health, considering the unique challenges of each domain and the diverse parameters outlined in “Evaluation methods”. The Leaderboard represents the final component of the evaluation framework, providing interacting users with the ability to rank and compare diverse healthcare chatbot models. It offers various filtering strategies, allowing users to rank models according to specific criteria. For example, users can prioritize accuracy scores to identify the healthcare chatbot model with the highest accuracy in providing answers to healthcare questions. Additionally, the leaderboard allows users to filter results based on confounding variables, facilitating the identification of the most relevant chatbot models for their research study.

Advanced analytics solutions are also critical for effectively utilizing newer types of patient data, such as insights from genetic testing. In June 2023, research published in Science Advances demonstrated the potential for AI-enabled drug discovery. The study authors found that a generative AI model could successfully design novel molecules to block SARS-CoV-2, the virus that causes COVID-19. They noted that the tool — used to study aneurysms that ruptured during conservative management — could accurately identify aneurysm enlargement not flagged by standard methods. The potentially life-threatening nature of aneurysm rupture makes effective monitoring and growth tracking vital, but current tools are limited.

Many factors contribute to low COVID-19 vaccination coverage, including vaccine supply and distribution, access to healthcare facilities, and vaccine hesitancy. Individual attitudes and subsequent behavioral tendencies are commonly thought to be influenced by prototypical similarity and favorability (Lane and Gibbons, 2007; Branley and Covey, 2018). Prototypical similarity is the degree of similarity ChatGPT between the individual’s perceived self and the prototype, and is usually assessed by the individual’s response to the question “How similar are you to the prototype? Prototypical favorability is considered to be an individual’s intuitive attitudinal evaluation toward a certain group or behavior, the assessment of which usually involves adjectival descriptors (Gibbons and Gerrard, 1995).

In all three locations, participants were recruited by Premise, a participant recruitment and market research company70, via random sampling using existing online panels. Performance metrics are essential in assessing the runtime performance of healthcare conversational models, as they significantly impact the user experience during interactions. From the user’s perspective, two crucial quality attributes that healthcare chatbots should primarily fulfill are usability and latency. Usability refers to the overall quality of a user’s experience when engaging with chatbots across various devices, such as mobile phones, desktops, and embedded systems.

Among the 172 key messages, ChatGPT-3.5 addressed 13 key messages completely and failed to address 123, whereas ChatGPT-4 addressed 20 key messages completely and did not address 132. Both versions of ChatGPT more frequently addressed BLS key messages completely than they did key messages from other chapters. In all the other chapters, more than two-thirds of the key messages were not addressed at all (Fig. 1). In response to inquiries about the five chapters, ChatGPT-3.5 generated a total of 60 statements, whereas ChatGPT-4 produced 32. The number of statements generated by the AIs was fewer than the number of key messages for each chapter.

The chatbot can serve as a first point of call to collect data, particularly relating to embarrassing symptoms. However, it is important to acknowledge that further research is needed to investigate the safety and effectiveness of medical chatbots in real-world health settings. The popularization of AI in healthcare depends on the population’s acceptance of related technologies, and overcoming individual resistance to AI healthcare technologies such as health chatbots is crucial for their diffusion (Tran et al., 2019; Gaczek et al., 2023).

Owing to the lack of conceptual understanding, AI chatbots carry a high risk of disseminating misconceptions. The failure to reproduce a high percentage of the key messages indicates that the relevant text for the task was not part of the training texts of the underlying LLMs. Therefore, despite their theoretical potential, the tested AI chatbots are, for the moment, not helpful in supporting ILCOR’s mission for the benefits of chatbots in healthcare dissemination of current evidence, regardless of the user language. However, the active process of reception to understand a subject remains a fundamental prerequisite for developing expertise and making informed decisions in medicine. Therefore, all healthcare professionals should focus on literature supporting the understanding of the subject and refrain from trying to delegate this strenuous process to an AI.

Rather, Longhurst and McSwain say the chatbots are trained on specific medical and health databases. They can also securely consult certain parts of the patient’s electronic medical records to make sure they fully understand the person’s history. The service, Northwell Health Chats, is customized to each patient’s condition, medical history, and treatment. The chatbots send a message to start a conversation, posing a series of questions about the patient’s conditions, with choices of answers to click on or fill in. Healthcare professionals looking at the potential for AI advances to augment symptom checkers should be wary of how they incorporate data about patient health history. It would also be key to examine how AI impacts the patient-provider relationship and how learned bias can impact AI performance.

AI Chatbots Could Benefit Dementia Patients

Most companies aren’t publishing the data they use to train these models because they claim it’s proprietary. Another ethical issue that is often noticed is that the use of technology is frequently overlooked, with mechanical issues being pushed to the front over human interactions. The effects that digitalizing healthcare can have on medical practice are especially concerning, especially on clinical decision-making in complex situations that have moral overtones. Such self-diagnosis may become such a routine affair as to hinder the patient from accessing medical care when it is truly necessary, or believing medical professionals when it becomes clear that the self-diagnosis was inaccurate.

• For one question, ChatGPT offered an answer that was rooted in outdated information and practice.
• Still, many companies are developing their chatbots and generative artificial intelligence models for integration into health care settings—from medical scribes to diagnostic chatbots—raising broad-ranging concerns over AI regulation and liability.
• According to a 2021 article published in JMIR Cancer, there are five categories of chatbots that are suited to healthcare use cases.
• In recent years, the rise of predictive analytics has aided providers in delivering more proactive healthcare to patients.

This could include making the program available to those who are located in rural or remote areas or who are unable to access conventional mental healthcare due to financial, cultural, or other barriers. In a further sign of caution toward AI chatbots for mental health support, 46% of U.S. adults say these AI chatbots should only be used by people who are also seeing a therapist; another 28% say they should not be available to people at all. Just 23% of Americans say that such chatbots should be available to people regardless of whether they are also seeing a therapist. Among those who think that the problem of bias in health and medicine would stay about the same with the use of AI, 28% say the main reason for this is because the people who design and train AI, or the data AI uses, are still biased. About one-in-ten (8%) in this group say that AI would not change the issue of bias because a human care provider would be primarily treating people even if AI was adopted, so no change would be expected.

Patients can access reliable health advice 24/7, reducing the need for unnecessary visits to emergency departments or GP surgeries. Also, it’s a good idea to regularly test an AI chatbot’s protection measures and response to malicious requests. Generative AI is a rapidly developing technology, and hackers constantly come up with new ways to abuse it.

Some machine learning models have even shown promising results in detecting cancers at an early stage,7 potentially improving survival rates and reducing instances of misdiagnosis. Now, generative AI technology is augmenting this by automatically initiating processes such as filling in forms, and processing referrals or requisitions directly from a patient’s history. AI chatbots in the medical field have ushered in a new era in which the intersection of technology and medical care has potential to create a future that is coordinated, efficient, and focused on patients.

By 2024, the software segment spearheaded the healthcare chatbot market, contributing to a substantial revenue share exceeding 62.0%. Malicious actors can hack into conversational AI tools and divulge patients’ private data or personally identifiable information. This data includes both patients’ answers to an AI tool’s questions and questions that patients ask the AI tool. For example, if a patient asks an office AI chatbot to go over an aspect of their health records, that leaves their records open to an extraction hack, putting the hospital or pharmacy at risk of a lawsuit or fine.

ML algorithms and other technologies are used to analyze data and develop predictive models to improve patient outcomes and reduce costs. One area where predictive analytics can be instrumental is in identifying patients at risk of developing chronic diseases such as endocrine or cardiac diseases. By analyzing data such as medical history, demographics, and lifestyle factors, predictive models can identify patients at higher risk of developing these conditions and target interventions to prevent or treat them [61]. Predicting hospital readmissions is another area where predictive analytics can be applied. On the basis of our analysis, we can advise both AI chatbot users and educators of healthcare professionals on the risks and benefits of the tested AI chatbots.

Furthermore, there are potential privacy concerns with emerging technologies like chatbots offered to patients due to the discrepancy between standard medical care practices and technology’s terms of use66. Patients may not fully understand the implications of sharing personal information with chatbots, which may collect data beyond their expectations and control. It is also important to consider that vendors may not provide enough information to consumers about data privacy risks, while healthcare providers are aware of the issue but face challenges in properly managing the risk-67,68. Providers struggle to contract the risk properly, which may result in potential breaches of patient privacy.

The anticipated high market shares in both categories reflect a strategic alignment with contemporary technological trends, positioning the market to harness the benefits of software and cloud solutions in the coming years. “We have an AI model now that can incidentally say, ‘Hey, you’ve got a lot of coronary artery calcium, and you’re at high risk for a heart attack or a stroke in five or 10 years,’ ” says Bhavik Patel, M.D., M.B.A., the chief artificial intelligence officer at Mayo Clinic in Arizona. What you might not know is that AI has been and is being used for a variety of healthcare applications. Here’s a look at how AI can be helpful in healthcare, and what to watch for as it evolves. In many cases, conversational AI tools and the resources needed to operate them, such as data centers, can be cost prohibitive.

Overall, the use of AI in TDM has the potential to improve patient outcomes, reduce healthcare costs, and enhance the accuracy and efficiency of drug dosing. As this technology continues to evolve, AI will likely play an increasingly important role in the field of TDM. Emergency department providers understand that integrating AI into their work processes is necessary for solving these problems by enhancing efficiency, and accuracy, and improving patient outcomes [28, 29]. Additionally, there may be a chance for algorithm support and automated decision-making to optimize ED flow measurements and resource allocation [30].

This framework is intended to act as the foundational codebase for future benchmarks and guidelines. Notably, while recent studies50,68,69,70 have introduced various evaluation frameworks, it is important to recognize that these may not fully cater to the specific needs of healthcare chatbots. Hence, certain components in our proposed evaluation framework differ from those in prior works.

The claims management process is rife with labor- and resource-intensive tasks, such as managing denials and medical coding. To that end, many in the healthcare space are interested in AI-enabled autonomous coding, patient estimate automation and prior authorization technology. The researchers underscored that many patients stop mental health treatment following their first or second visit, necessitating improved risk screening to identify those at risk of a suicide attempt. However, the small number of visits that these patients attend leads to limited data being available to inform risk prediction. While new healthcare chatbots continue to surface, it is important not to overlook the remarkable ones that have paved the way for these innovations.