ChatGPT for Academic Purposes: Survey Among Undergraduate Healthcare Students in Malaysia

Some providers have already seen success using AI-enabled CDS tools in the clinical setting. By utilizing AI’s advanced pattern recognition capabilities, CDS tools can incorporate risk stratification and predictive analytics, allowing them to help clinicians make more informed, personalized treatment recommendations in high-value use cases like chronic disease management. This list details — in alphabetical order — the top 12 ways that AI has and will continue to impact healthcare. Healthcare AI has generated major attention in recent years, but understanding the basics of these technologies, their pros and cons, and how they shape the healthcare industry is vital. Unlike other performance measurement studies, we imitated the actual usage behaviour of inexperienced users.

By contrast, large shares of Americans say they would not want any of the three other AI-driven applications used in their own care. On balance, those who see bias based on race or ethnicity as a problem in health and medicine think AI has potential to improve the situation. About half (51%) of those who see a problem think the increased use of AI in health care would help reduce bias and unfair treatment, compared with 15% who say the use of AI would make bias and unfair treatment worse.

Healthcare

Researchers have found that, although patients are open to being screened by symptom checkers embedded in chatbots, they are only satisfied with the care when the bots seem competent. In 2017, CityMD published survey data indicating that patients don’t always know when they should go to the emergency department versus the urgent care center. But online symptom checkers should fix this, both by giving patients a possible diagnosis and, in many cases, telling patients how to proceed.

As demand for virtual care solidifies, healthcare organizations are increasingly relying on various technologies to deliver care remotely. These include audio-visual technology, healthcare wearables, Bluetooth-enabled devices, and chatbots. Chatbots are well equipped to help patients get their healthcare insurance claims approved speedily and without hassle since they have been with the patient throughout the illness. Not only can they recommend the most useful insurance policies for the patient’s medical condition, but they can save time and money by streamlining the process of claiming insurance and simplifying the payment process. Notably, the integration of chatbots into healthcare information websites, exemplified by platforms such as WebMD, marked an early stage where chatbots aimed to swiftly address user queries, as elucidated by Goel et al. (2).

For example, these studies unable to assess chatbots in terms of empathy, reasoning, up-to-dateness, hallucinations, personalization, relevance, and latency. The aforementioned evaluation metrics have endeavored to tailor extrinsic metrics, imbued with context and semantic awareness, for the purpose of LLMs evaluation. However, each of these studies has been confined to a distinct set of metrics, thereby neglecting to embrace the comprehensive and all-encompassing aspect concerning ChatGPT healthcare language models and chatbots. The report from The University of Arizona Health Sciences showed that around half of patients don’t fully trust AI-powered medical advice, like the information issued from chatbots like ChatGPT. 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.

One of the prevalent challenges in drug development is non-clinical toxicity, which leads to a significant percentage of drug failures during clinical trials. However, the rise of computational modeling is opening up the feasibility of predicting drug toxicity, which can be instrumental in improving the drug development process [46]. This capability is particularly vital for addressing common types of drug toxicity, such as cardiotoxicity and hepatotoxicity, which often lead to post-market withdrawal of drugs. AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making.

As the hype around generative AI continues, healthcare stakeholders must balance the technology’s promise and pitfalls.

This post explores the opportunities and challenges of using AI chatbots for mental health. The chatbot from Antara Health is a prime example of the personalized care plan strategy. By analyzing patient data to monitor health progress, modify medication, and offer tailored recommendations, it aids in the management of chronic diseases. By doing this, it maximizes long-term health outcomes by ensuring that patients follow their treatment plans and aids in preventing complications.

If innovation is likely to disrupt daily routines and conflict with established behavioral patterns and customs, individuals may refuse to utilize it and thus develop resistance behavior (Ram, 1987). Subsequently, Ram and Sheth (1989) revised the IRT by proposing that two particular barriers perceived by individuals when confronted with innovation, namely, functional and psychological barriers, result in their resistance behavioral tendency. AI algorithms can continuously examine factors such as population demographics, disease prevalence, and geographical distribution. This can identify patients at a higher risk of certain conditions, aiding in prevention or treatment. Edge analytics can also detect irregularities and predict potential healthcare events, ensuring that resources like vaccines are available where most needed.

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Health chatbots are revolutionizing personal healthcare practices (Pereira and Díaz, 2019). Currently, health chatbots are utilized for personal health monitoring and disease consultation, diagnosis, and treatment (Tudor Car et al., 2020; Aggarwal et al., 2023). Further, “Tess” is a mental health chatbot that provides personalized medical suggestions to patients with mental disorders (Gionet, 2018), similar to a therapist. Remarkably, a personal health assistant aimed at preventative healthcare, “Your.MD,” has thus far been used to provide diagnostic services and solutions to nearly 26 million users worldwide (Billing, 2020). According to BIS Research, the global market for healthcare chatbots is expected to reach $498.1 million by 2029 (Pennic, 2019). The potential applications of AI in assisting clinicians with treatment decisions, particularly in predicting therapy response, have gained recognition [49].

The report guides 10 stages of AI chatbot development, beginning with concept and planning, including safety measures, structure for preliminary testing, governance for healthcare integration and auditing and maintenance and ending with termination. Only about half of the respondents in the DUOS survey knew the difference between Medicare and Medicare Advantage. As chatbots become more sophisticated, they will empower patients to take a more active role in their health management.

TDM aims to ensure that patients receive the right drug, at the right dose, at the right time, to achieve the desired therapeutic outcome while minimizing adverse effects [56]. The use of AI in TDM has the potential to revolutionize how drugs are monitored and prescribed. AI algorithms can be trained to predict an individual’s response to a given drug based on their genetic makeup, medical history, and other factors. This personalized approach to drug therapy can lead to more effective treatments and better patient outcomes [57, 58]. In recent years, the rise of predictive analytics has aided providers in delivering more proactive healthcare to patients. In the era of value-based care, the capability to forecast outcomes is invaluable for developing crucial interventions and guiding clinical decision-making.

Tools like biosensors and wearables are frequently used to help care teams gain insights into a patient’s vital signs or activity levels. In addition to predictive analytics, AI tools have advanced the field of remote patient monitoring. AI technologies are already changing medical imaging by enhancing screening, risk assessment and precision medicine. Addressing these challenges requires health systems to juggle staffing restrictions with surgeon preferences, which data analytics and AI can help with.

In the healthcare arena, patients may be tempted to tell their symptoms to a chatbot rather than a physician, and clinicians may be able to leverage these tools to easily craft medical notes and respond to portal messages. Healthcare organizations and other groups have also drafted guidelines to help providers and payers navigate these challenges. Recently, the National Academy of Medicine released its AI Code of Conduct, which brought together researchers, patient advocates and others to outline the national architecture needed to promote the responsible, equitable use of these technologies in healthcare. Alongside these issues, a March 2024 study in the Journal of Medical Internet Research revealed that generative AI poses major security and privacy risks that could threaten patients’ protected health information.

Chatbots for embarrassing and stigmatizing conditions: could chatbots encourage users to seek medical advice?

Yet some of the chatbot answers were off base from the question or contained factual errors. In the past, patients might call their family practice to list their symptoms and seek advice about how to proceed. You can foun additiona information about ai customer service and artificial intelligence and NLP. That might still be the best option for extremely complex cases, but a symptom checker that leverages AI should be able to triage a patient exhibiting the common cold.

Particularly, genomics plays a key role in precision and personalized medicine, but making these insights useful requires analyzing large, complex datasets. EHR adoption aims to streamline clinical workflows while bolstering cost-effective care delivery, but instead, clinicians are citing clinical documentation and administrative tasks as sources of EHR burden and burnout. The key messages were treated methodologically as equivalent, as there is no tool to compare the clinical relevance of the individual statements against each other, even if some statements appear more important than others.

How can admissions officers respond to chatbot compositions?

Regulatory bodies like the Food and Drug Administration (FDA) in the US or the European Medicines Agency (EMA) in Europe have rigorous processes for granting approval to AI chatbot-based medical devices and solutions. These processes, while critical for ensuring safety and efficacy, can be time-consuming and resource-intensive. Trust AI assumes a critical role in navigating complexities, particularly in AI-powered chatbots.

The implementation of mitigation strategies for the use of ChatGPT and ChatGPT-supported chatbots presents several challenges, ranging from technical and ethical considerations to user experience and bias mitigation. Furthermore, users must understand benefits of chatbots in healthcare the difference between AI-generated therapy and AI-guided therapy when accessing digital tools for supporting mental health. ChatGPT and ChatGPT-supported chatbot offer a low-barrier, quick access to mental health support but is limited in approach.

• However, interaction with health professionals often requires traditional on-site (in-person) visits, and substantial time, travel and financial costs for patients12.
• To meet the highest standards of care in medicine, an algorithm should not only provide an answer, but offer a correct one—clearly and effectively.
• This conclusion is similar to that of prior studies, such as Kautish et al. (2023), who found that functional barriers to telemedicine apps play a more predictable role in users’ purchase resistance intentions.
• The chatbot uses advanced algorithms to analyze patients’ symptoms, medical history, and other relevant information to provide tailored health advice and recommendations.
• The potential applications of AI in assisting clinicians with treatment decisions, particularly in predicting therapy response, have gained recognition [49].

Called ELIZA, the chatbot simulated a psychotherapist, using pattern matching and template-based responses to converse in a question-based format. Dr. Liji Thomas is an OB-GYN, who graduated from the ChatGPT App Government Medical College, University of Calicut, Kerala, in 2001. Liji practiced as a full-time consultant in obstetrics/gynecology in a private hospital for a few years following her graduation.

• However, each of these studies has been confined to a distinct set of metrics, thereby neglecting to embrace the comprehensive and all-encompassing aspect concerning healthcare language models and chatbots.
• Instead, they serve as valuable tools to assist and augment medical staff’s capabilities.
• The comparative analysis of the key messages of the guidelines and the AI-generated statements revealed a discordance between the key messages and the statements in terms of number and content.
• Therefore, it is crucial to eliminate people’s instinctive negative views of health chatbots for their social popularization.
• AI is also useful when healthcare organizations move to new EHR platforms and must undertake legacy data conversion.
• Based on deployment, the cloud based segment occupied the largest share and is also the fastest growing segment during the forecast period owing to various advantages offered by these type of chatbots.

A symptom checker could also alert patients when they should seek urgent or emergency care. Google” has led many patients to seek out health information online when they feel symptoms. Online medical research has become more common, with 60 percent of doctors saying in a 2018 Merck Manuals survey that they have noticed more patients coming in with information about their symptoms that they got online. Table 1 offers a detailed explanation of the advantages of ChatGPT in transforming mental healthcare (Miner et al., 2019; Denecke et al., 2021; Cosco, 2023; Nothwest Executive Education, 2023). Black adults are especially likely to say that bias based on a patient’s race or ethnicity is a major problem in health and medicine (64%). A smaller share of White adults (27%) describe bias and unfair treatment related to a patient’s race or ethnicity as a major problem in health and medicine.

Guest Authors contribute insightful and knowledgeable articles to Techloy about a product, service, feature, topic, or trend. Like any technology, generative AI presents multiple potential pitfalls alongside its possibilities. Generative AI tools are also creating a buzz in revenue cycle management and health insurance.