Will chatbots help or hamper medical education? Here is what humans and chatbots say

Despite this, many health systems are increasingly prioritizing AI initiatives, with experts predicting that generative AI will continue to make a splash in healthcare. A recent survey commissioned by Wolters Kluwer Health found that physicians are cautiously optimistic about generative AI, while a report from John Snow Labs revealed that healthcare and life sciences organizations are increasingly investing in the tools. The proposed metrics demonstrate both within-category and between-category associations, with the potential for negative or positive correlations among them.

Overall, changing these behaviours requires sustained intervention, which can be cost-, time- and resource-intensive13. Therefore, cost-effective and feasible behaviour change interventions are required to reduce the prevalence of physical inactivity, poor diet and poor sleep. In the landscape of digital health, AI-powered chatbots have emerged as transformative tools, reshaping the dynamics of telemedicine and remote patient monitoring. These innovations hold great promise for expanding healthcare access, enhancing patient outcomes, and streamlining healthcare systems.

Researchers writing recently in the Journal of Medical Systems demonstrated that ChatGPT may enhance geriatric polypharmacy management and deprescription by providing clinical decision support to primary care physicians. Some of the most promising applications for generative AI are related to electronic health records (EHRs) and workflow optimization. EHR vendors are utilizing the technology to summarize patient information, speed up patient portal messaging, generate hospital discharge summaries and streamline clinical documentation.

In a November 2023 interview with PharmaNewsIntelligence, leadership from QuartzBio, part of Precision for Medicine, indicated that stakeholders must prioritize privacy, security and model validation to successfully integrate AI into clinical trials. AI tools can be used to streamline data collection and management, break down data silos, optimize trial enrollment and more in medical research. The tool is designed to identify B-lines — bright, vertical image abnormalities that indicate inflammation in patients with pulmonary complications — to diagnose COVID-19 infection with a high degree of accuracy. A March 2024 study published by Johns Hopkins researchers in Communications Medicine showed that a deep neural network-based automated detection tool could assist emergency room clinicians in diagnosing COVID-19 by analyzing lung ultrasound images. Capacity management is a significant challenge for health systems, as issues like ongoing staffing shortages and the COVID-19 pandemic can exacerbate existing hospital management challenges like surgical scheduling. However, monitoring and managing all the resources required is no small undertaking, and health systems are increasingly looking to data analytics solutions like AI to help.

A study conducted by Huang et al. where authors utilized patients’ gene expression data for training a support ML, successfully predicted the response to chemotherapy [51]. In this study, the authors included 175 cancer patients incorporating their gene-expression profiles to predict the patients’ responses to various standard-of-care chemotherapies. Notably, the research showed encouraging outcomes, achieving a prediction accuracy of over 80% across multiple drugs. In another study performed by Sheu et al., the authors aimed to predict the response to different classes of antidepressants using electronic health records (EHR) of 17,556 patients and AI [52].

Moreover, training is essential for AI to succeed, which entails the collection of new information as new scenarios arise. However, this may involve the passing on of private data, medical or financial, to the chatbot, which stores it somewhere in the digital world. You can foun additiona information about ai customer service and artificial intelligence and NLP. Chatbots called virtual assistants or virtual humans can handle the initial contact with patients, asking and answering the routine questions that inevitably come up. During the coronavirus disease 2019 (COVID-19) pandemic, especially, screening for this infection by asking certain questions in a certain predefined order, and thus assessing the risk of COVID-19 could save thousands of manual screenings.

Intervention

All of these benefits assume that symptom checkers produce the correct diagnosis every time. But data has shown that these tools are still imperfect and that there are limits to their potential. Online symptom checkers, often embedded in chatbots, are promising because they can efficiently triage patients. When patients describe symptoms of the common cold, the symptom checker should respond by saying patients may ride out the symptoms at home with some over-the-counter remedies.

• The research, however, found that chatbot effectiveness is only as good as the medical knowledge used in their programming and the quality of the user’s interactions.
• Even if the review process is perfect, however, specific algorithms might escape regulation as medical devices.
• Given the potential for adverse outcomes, it becomes imperative to ensure that the development and deployment of AI chatbot models in healthcare adhere to principles of fairness and equity (16).
• This process helps the chatbot determine the urgency of the patient’s condition and guide them to the most suitable course of action, whether it’s self-care advice, scheduling an appointment, or directing them to emergency services.

Healthcare organizations are struggling with high demand for medical services and short staffing. This makes healthcare professionals overworked, burned out and tempted to leave their jobs. Many believe that AI-based solutions like chatbots can reduce the load on healthcare professionals and make medical help more accessible to patients. Healthcare chatbots are artificial intelligence based used interfaces that are employed to create a conversation medium between a machine and a human. These chatbots are employed in assessment of symptoms of a patient before a physician visit.

The guarantor (L.L.) accepts full responsibility for the work, had access to the data, and controlled the decision to publish. From February 11th, 2022 to June 30th, 2022, 2045 participants were enrolled and randomly assigned to the control and intervention groups. After excluding 1,280 participants who were lost to follow-up, responses from 748 participants were included in the final analysis.

Unleashing AI’s Power: Chatbots Transforming Healthcare Experiences

Americans anticipate a range of positive and negative effects from the use of AI in health and medicine. When studying the apps, the team looked at productivity, effectiveness, functionality and humanity, and overall satisfaction. The dominance of software and the emphasis on cloud-based deployment underscore the industry recognition of the pivotal role of technology in driving efficiency and innovation.

The AI models considered features predictive of treatment selection to minimize confounding factors and showed good prediction performance. The study demonstrated that antidepressant response could be accurately predicted using real-world EHR data with AI modeling, suggesting the potential for developing clinical decision support systems for more effective treatment selection. While considerable progress has been made in leveraging AI techniques and genomics to forecast treatment outcomes, it is essential to conduct further prospective and retrospective clinical research and studies [47, 50]. These endeavors are necessary for generating the comprehensive data required to train the algorithms effectively, ensure their reliability in real-world settings, and further develop AI-based clinical decision tools. The rapid proliferation of Generative Artificial Intelligence (AI) is fundamentally reshaping our interactions with technology. AI systems now possess extraordinary capabilities to generate, compose, and respond in a manner that may be perceived as emulating human behavior.

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. Ultimately, AI and innovation go hand-in-hand, making it an asset to the field of medicine — when used judiciously. Medical advancements depend on continuously learning from novel insights, and AI empowers innovators to work more quickly and accurately with more extensive data. While evolving technologies must be wielded with care, they have already found a place within medical toolkits.

Chatbots play a critical role in virtual care delivery as they can be deployed in various ways to improve healthcare access and patient experience. They can be informative, providing information from databases or inventories; conversational, conversing with users as naturally as possible; or task-based, performing specific pre-determined actions. Besides answering questions related to illness, medications and common occurrences during the course of a chronic condition, chatbots can help evaluate how a patient is doing during follow-up, and schedule an appointment with the physician where further care is required.

“Older adults will forgo health services because they don’t think they can afford them,” says Ulfers. AI tools can help seniors understand their benefits and can simplify the process of receiving care. “We find that 82% of older adults ChatGPT have not had an annual wellness visit. AI can help them find financial benefits for transportation, food and other medical care needs.” Not everyone has the time or energy to search through paperwork or complex government websites.

Moreover, the nonjudgmental nature of a chatbot can make users feel more comfortable sharing their thoughts and feelings. This can lead to more honest and open conversations, essential for adequate mental health support. The applications continue to expand into areas such as treatment planning.8 In 2023, Google announced its partnership with the Mayo Clinic to develop an AI solution for radiotherapy treatment planning. This collaboration aims to use AI technology to analyze patient data and help physicians create personalized treatment plans more efficiently — potentially improving outcomes and reducing side effects.

Are individuals more inclined towards AI than human healthcare providers

By collectively addressing these factors, the interpretation of metric scores can be standardized, thereby mitigating confusion when comparing the performance of various models. The integration of the aforementioned requirements should result in the desired scores, treating the evaluation component as a black box. Nevertheless, an unexplored avenue lies in leveraging BERT-based models, trained on healthcare-specific categorization and scoring tasks. By utilizing such models, it becomes possible to calculate scores for individual metrics, thereby augmenting the evaluation process.

A Journal of Medical Internet Research study pointed out that while chatbots can support mental health care, they should not replace professional diagnosis and treatment (Vaidyam and colleagues, 2019). One of the primary benefits of AI chatbots in mental health care is their enhanced accessibility and ability to provide immediate support. Traditional mental health services often require appointments, which can involve long waiting periods. In contrast, AI chatbots are available 24/7, offering instant support regardless of the time or location.

Artificial intelligence (AI) driven chatbots, such as ChatGPT, have garnered attention by successfully passing the U.S. Medical Licensing Examination (USMLE) and knowledge assessments in Basic life support (BLS) and Advanced life support (ALS) [2,3,4,5,6]. Numerous publications underscore the vast potential these technologies hold for enhancing patient care, augmenting diagnostic capabilities, and shaping the future landscape of medicine [7,8,9,10] . Technical journals highlight the capacity of ChatGPT and its prospective role as a clinical decision aid, offering real-time, evidence-based recommendations to healthcare practitioners [10, 11].

Some scholars, however, have indicated that “utility maximization” does not always serve as a criterion for people’s actions, and the rational paradigm may not competently explain people’s decision-making behavior (Baron, 1994; Yang and Lester, 2008). However, few studies have examined the influence of irrational motivations and psychological mechanisms on health chatbot resistance behaviors. As such, the second research question of this study was to explore the psychological mechanisms behind people’s resistance to health chatbots. Despite the numerous potential benefits of health chatbots for personal health management, a substantial proportion of people oppose the use of such software applications. The integration of AI in healthcare has immense potential to revolutionize patient care and outcomes.

In order to recruit more participants, we relaxed our study criteria to include those who delayed their vaccination until the implementation of governmental vaccine mandates. Another possible reason for our small sample size is the high proportions of participants lost to follow-up, potentially due to our chatbot’s design31. For instance, our chatbot was not able to recognize users’ emotions and tailor phrase responses to questions. In addition, since participants recruited by Premise were more familiar with surveys related to market research rather than vaccines, their indifference to domains of chatbot contents might have led to user dissatisfaction and consequently a high drop-out rate. Second, our sample population was not representative of the populations in respective regions.

A significant relationship exists between performance metrics and the other three categories. For instance, the number of parameters in a language model can impact accuracy, trustworthiness, and empathy metrics. An increase in parameters may introduce complexity, potentially affecting these metrics positively or negatively.

The growth indicates the increasing adoption of healthcare chatbots, driven by rising demand for virtual health assistance, advancements in AI and NLP technologies, and a growing emphasis on patient engagement. The TCS study emphasizes balancing multiple strategic objectives when implementing AI in healthcare. As a result, organizations are encouraged to simultaneously pursue optimization, productivity, innovation, and quality. Healthcare providers can continuously improve their processes by leveraging AI and staying ahead of the curve. Now, machine learning has filled in that gap in collective knowledge by pulling together all this patient data and distilling it down into one location.

And finally, patients may feel alienated from their primary care physician or self-diagnose once too often. The widespread use of chatbots can transform the relationship between healthcare professionals and customers, and may fail to take the process of diagnostic reasoning into account. This process is inherently uncertain, and the diagnosis may evolve over time as new findings present themselves. Chatbots can be exploited to automate some aspects of clinical decision-making by developing protocols based on data analysis. Many individuals avoid reaching out to mental health professionals due to fear of judgment or embarrassment.

People’s feelings about AI replacing or augmenting human healthcare practitioners, its role in educating and empowering patients, and its impact on the quality and efficiency of care, as well as on the well-being of healthcare workers, are all important considerations. In medicine, patients often trust medical staff unconditionally and believe that their illness will be cured due to a medical phenomenon known as the placebo effect. In other words, patient-physician trust is vital in improving patient care ChatGPT App and the effectiveness of their treatment [105]. For the relationship between patients and an AI-based healthcare delivery system to succeed, building a relationship based on trust is imperative [106]. Furthermore, these tools can always be available, making it easier for patients to access healthcare when needed [84]. Another medical service that an AI-driven phone application can provide is triaging patients and finding out how urgent their problem is, based on the entered symptoms into the app.

However, the researchers conducting this study emphasize that their results only suggest the value of such chatbots in answering patients’ questions, and recommend it be followed up with a more convincing study. AI is poised to revolutionize the industry by enhancing diagnostic accuracy, improving surgical precision, boosting productivity, and maintaining high-quality standards. However, to fully realize these benefits, healthcare providers must develop cohesive AI strategies, establish clear KPIs, and navigate the regulatory landscape carefully. With the right approach and regulations, AI can elevate healthcare to new heights, driving both operational excellence and improved patient outcomes. Already, healthcare providers, surgeons and researchers are using AI to develop new drugs and treatments, diagnose complex conditions more efficiently and improve patients’ access to critical care — and this is only the beginning.

Patient engagement plays a major role in improving health outcomes by enabling patients and their loved ones to be actively involved in care. Often, patient engagement solutions are designed to balance convenience and high-quality interpersonal interaction. To tackle this, both health systems have implemented a cloud-based capacity management platform to support scheduling optimization. The tool uses data on surgery type, length and other information to help staff streamline OR scheduling, which has led to improvements in primetime OR utilization and proactively released OR time. Typically, inconsistencies pulled from a medical record require data translation to convert the information into the ‘language’ of the EHR. The process usually requires humans to manually translate the data, which is not only time-consuming and labor-intensive but can also introduce new errors that could threaten patient safety.

But trust is critical for AI chatbots in healthcare, according to healthcare leaders and they must be scrupulously developed. Money-saving AI chatbots in healthcare were predicted to be a crawl-walk-run endeavor, where easier tasks are moved to chatbots while the technology advanced enough to handle more complex tasks. Stakeholders also said that the use of chatbots to expand healthcare access must be implemented in existing care pathways, should “not be designed to function as a standalone service,” and may require tailoring to align with local needs.

Drug discovery, development and manufacturing have created new treatment options for a variety of health conditions. Integrating AI and other technologies into these processes will continue revolutionizing the pharmaceutical industry. By measuring and reporting interrater reliability, a quality indicator of comparative studies, we inadvertently discovered that poor interrater reliability might indicate insufficient training of the LLM on the topic.

AI in enhancing patient education and mitigating healthcare provider burnout

Woebot has proved successful in empowering individuals to manage their mental health more independently and has provided valuable insight to mental health professionals. Buoy Health leverages advanced algorithms to offer personalized medical advice based on user inputs. Launched in 2017, this interactive chatbot is capable of analyzing symptoms, assessing the severity of conditions, and guiding users to appropriate healthcare resources. Buoy Health reduces the burden on emergency departments by acting as a virtual triage tool. It helps users make more informed decisions about seeking medical advice, and saves valuable healthcare resources for those most in need.

She said carers might act on faulty or biased information and inadvertently cause harm, and an AI-generated care plan might be substandard. Receive free access to exclusive content, a personalized homepage based on your interests, and a weekly newsletter with the topics of your choice. Receive free access to exclusive content, a personalized homepage based on your interests, and a weekly newsletter with topics of your choice. The TCS study suggests that AI will continue to evolve, moving from assisting humans to augmenting and ultimately transforming human activities. Executives believe that human creativity and strategic thinking will remain essential for competitive differentiation in the next 3–5 years.

• These findings support the need for prospective validation through randomized clinical trials and indicate the potential of AI in optimizing chemotherapy dosing and lowering the risk of adverse drug events.
• 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.
• Those with higher levels of education and income, as well as younger adults, are more open to AI in their own health care than other groups.

By harnessing the power of artificial intelligence and machine learning, these intelligent virtual assistants transform how patients access medical advice, receive personalized recommendations, and navigate the healthcare system. One key advantage of AI-powered chatbots is their ability to support multiple languages, making healthcare services more accessible to diverse patient populations. Chatbots can be programmed to understand and respond in various languages, breaking down communication barriers and ensuring that patients from different linguistic backgrounds receive equal access to triage services. This inclusive approach promotes health equity and helps healthcare organizations serve a broader range of patients effectively. AI-powered chatbots are transforming patient triage by significantly reducing waiting times.

It’s up to the radiologist to review the 3D images and search for areas of density, calcifications (which can be early signs of cancer), architectural distortion (areas where tissue looks like it’s pulling the surrounding tissue) and other areas of concern. An example of Cleveland Clinic’s commitment to AI innovation is the Discovery Accelerator, a 10-year strategic partnership between IBM and Cleveland Clinic, focused on accelerating biomedical discovery. Artificial intelligence describes the use of computers to do certain jobs that once required human intelligence. Examples include recognizing speech, making decisions and translating between different languages.

Faster clinical data interpretation is crucial in ED to classify the seriousness of the situation and the need for immediate intervention. The risk of misdiagnosing patients is one of the most critical problems affecting medical practitioners and healthcare systems. A study found that diagnostic errors, particularly in patients who visit the ED, directly contribute to a greater mortality rate and a more extended hospital stay benefits of chatbots in healthcare [32]. Fortunately, AI can assist in the early detection of patients with life-threatening diseases and promptly alert clinicians so the patients can receive immediate attention. Lastly, AI can help optimize health care sources in the ED by predicting patient demand, optimizing therapy selection (medication, dose, route of administration, and urgency of intervention), and suggesting emergency department length of stay.