ChatGPT Dependency Disorder in Healthcare Practice: An Editorial

Secondary outcomes included differences in perceived risks and benefits of COVID-19 vaccines and participants’ awareness and knowledge of COVID-19 vaccine-related misinformation before and after the intervention. For the chatbot evaluation, outcomes were described based on the RE-AIM framework criteria (Supplementary Table 13), and conversation contents of D24H chatbot and ChatSure in local languages were presented in word clouds (Supplementary Figs. 4–7). Future chatbots may improve on their vaccine promotion and communication strategies as well as their message delivery, such as by using an emotion-based approach that can convey reassurances to chatbot users to ameliorate their doubts and fears28,57. An anthropomorphic chatbot that can share anecdotes may also have a positive impact; Loft et al. found that personal stories that go beyond facts and traditional sources of authority can be more persuasive in online communications campaigns30.

Additionally, the technical environment in Africa, which is marked by patchy internet access and a still-developing digital infrastructure, can make it harder for AI chatbots to work and be integrated smoothly. In addition to the lack of experience or comprehension of the technology, mistrust towards AI-driven medical advice in Africa can also stem from concerns related to extractive data practices. The continent has faced challenges regarding data privacy and governance, with instances of data being collected without proper consent or transparency. This has led to a sense of apprehension among the population, as they may fear that their personal health information could be exploited or misused.

The others are ‘semi-guided conversation,’ which allows users to communicate with the chatbot with pre-defined responses and sometimes allows open inputs, and ‘open-ended conversation,’ which enables users to communicate with the chatbot with pre-defined responses and open inputs. Some chatbots incorporate human aid in their operations to provide more flexibility in clinical interventions. Over-reliance on chatbots may also give the green signal to healthcare benefits of chatbots in healthcare companies to follow the bait of market logic, making profits rather than benefiting the patient as the primary outcome, and allowing such companies to dominate healthcare at the cost of ethical function. And if there is a short gap in a conversation, the chatbot cannot pick up the thread where it fell, instead having to start all over again. This may not be possible or agreeable for all users, and may be counterproductive for patients with mental illness.

By doing so, this review aims to contribute to a better understanding of AI’s role in healthcare and facilitate its integration into clinical practice. The integration of ChatGPT and ChatGPT-supported chatbots opens avenues for expanding mental healthcare services to a larger population. To maximize their impact, ChatGPT and ChatGPT-supported chatbots should be part of a comprehensive mental health care that includes screening, continuous care and follow-up. It is essential to train ChatGPT and ChatGPT supported chatbots for a seamless transition to human professionals for diagnosis, treatment and for providing additional resources beyond the chatbot’s capabilities.

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The model’s success suggests that a similar approach could be applied to other serious conditions, like heart failure, to diagnose patients efficiently at the point of care. AI has given healthcare organizations a unique opportunity to overcome some of these hurdles, and some already see the benefits. EHRs hold vast quantities of information about a patient’s health and well-being in structured and unstructured formats. These data are valuable for clinicians, but making them accessible and actionable has challenged health systems. AI and ML, in particular, are revolutionizing drug manufacturing by enhancing process optimization, predictive maintenance and quality control while flagging data patterns a human might miss, improving efficiency. In the early days of CDS tools, many were standalone solutions that were not well-integrated into clinical workflows.

Medical chatbots could provide users with a more accessible initial consultation to discuss health concerns and/or medical symptoms (Bates, 2019). Medical chatbots could be used to encourage users to talk about their symptoms in a relaxed environment, which may act as a positive “first step” to help them on their health journey. Conducting some of the initial awkward discussions about embarrassing and/or stigmatizing symptoms through a medical chatbot could make the difference between someone seeking medical advice or choosing to ignore the issue (or delaying help seeking).

AI is advancing the state of healthcare

AI chatbots offer a private and anonymous space for users to express their feelings and thoughts without fear of judgment. This anonymity can encourage more individuals to seek help and engage in conversations about their mental health, potentially leading to earlier intervention and better outcomes. AI algorithms can analyze vast amounts of data in record time to assist with diagnosis, identifying patterns or anomalies that may not be easily seen by the human eye.

Providers who are interested in using ChatGPT or similar AI chatbots may consider outlining how the tools work, and how accurate they are, for their patients. Said otherwise, offering some sense of the human touch makes patients feel more comfortable with AI chatbots issuing medical advice. Database searches were limited to peer-reviewed journal articles published in English-language from inception until 1st September, 2022. Researchers tested six mHealth apps targeting dementia and found that they did not meet the needs of patients or their caregivers, according to a study published in 2021. The study revealed that the apps didn’t offer enough content to be attractive or useful, and they weren’t helpful for caregivers. A 2023 study found that chatbots can be effective in treating people with methamphetamine (MA) use disorder.

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After years on the market, online symptom checkers and patient triage tools are in the spotlight thanks to trends toward patient self-service and advances in artificial intelligence (AI). Even among Americans who say they have heard about these chatbots prior to the survey, 71% say they would not want to use one for their own mental health support. Public reactions to the idea of using an AI chatbot for mental health support are decidedly negative. About eight-in-ten U.S. adults (79%) say they would not want to use an AI chatbot if they were seeking mental health support; far fewer (20%) say they would want this. At this stage of development, a modest share of Americans see AI delivering improvements for patient outcomes.

While there is a whole different branch of AI that can help doctors provide diagnoses and identify treatment options, conversational AI shows promise in the area of automation as well. These tools may be able to handle much of the rote process where doctors, nurses and even pharmacists must give instructions to a patient, for instance. They would be able to not just repeat but rephrase instructions if needed for patients without running out of patience the way a human might.

Is ChatGPT ready to change mental healthcare? Challenges and considerations: a reality-check

Studies show that AI monitoring tools have been beneficial when it comes to seeing if patients are using medications like inhalers or insulin pens the way they’re prescribed and providing much-needed guidance when questions arise. For example, in the case of lung cancer, it’s common for oncologists to begin tracking the growth of nodules before they’re proven to be cancerous. You can foun additiona information about ai customer service and artificial intelligence and NLP. Let’s say you show a computer program a series of X-rays that may or may not show bone fractures. Then, you feed it another series of X-rays and have it rerun the program again with that new knowledge. Each time this process occurs, it’s able to make those decisions faster, more efficiently and more effectively. Broken bones, breast cancer, brain bleeds — these conditions and many others, no matter how complex, need the right kind of tools to make a diagnosis.

“The AI software kicks off a series of communications to make sure everyone in the chain — all the doctors, neurosurgeons, neurologists, radiologists and so on — are aware that this is happening and we’re able to expedite care,” he continues. It’s integrated into scheduling software, so it knows who’s on call and which doctors need to be notified right away. Now, out in the field, if EMS gets a call that they’re dealing with a possible stroke, they have the capability to trigger a stroke alert. This alert sets off a cascade of management events that prepares a team for a patient’s arrival and treatment plan — available surgeons are alerted, beds are made available, rooms are prepped for surgery, and so on.

However, individuals’ decisions to accept healthcare innovations are not necessarily reasonable or logical. In healthcare, guidelines usually take much time, from establishing the knowledge gap that needs to be fulfilled to publishing and disseminating these guidelines. AI can help identify newly published data based on data from clinical trials and real-world patient outcomes within the same area of interest which can then facilitate the first stage of mining information. On the contrary, a novel dose optimization system—CURATE.AI—is an AI-derived platform for dynamically optimizing chemotherapy doses based on individual patient data [55]. A study was conducted to validate this system as an open-label, prospective trial in patients with advanced solid tumors treated with three different chemotherapy regimens. CURATE.AI generated personalized doses for subsequent cycles based on the correlation between chemotherapy dose variation and tumor marker readouts.

Vagelis Hristidis is corresponding author and a professor of computer science in UC Riverside’s Marlan and Rosemary Bourns College of Engineering. By 2024, the healthcare chatbot landscape is anticipated to be valued at US$ 1.1 billion. However, some care managers fear that using AI tech creates a risk that they will inadvertently break rules and lose their licence. Mark Topps, who works in social care and co-hosts The Caring View podcast, said people working in social care were worried that by using technology they might inadvertently break Care Quality Commission rules and lose their registration.

Collaboration among stakeholders is vital for robust AI systems, ethical guidelines, and patient and provider trust. Continued research, innovation, and interdisciplinary collaboration are important to unlock the full potential of AI in healthcare. With successful integration, AI is anticipated to revolutionize healthcare, leading to improved patient outcomes, enhanced efficiency, and better access to personalized treatment and quality care. Firstly, comprehensive cybersecurity strategies and robust security measures should be developed and implemented to protect patient data and critical healthcare operations.

• These efforts aim to strike a balance between leveraging the power of AI chatbots for improved healthcare outcomes while safeguarding the privacy and confidentiality of sensitive patient information.
• Conducting thorough user testing, providing adequate staff training, and engaging in continuous monitoring and improvement are crucial for seamless integration and long-term success.
• Critics allege that black box tools — in which the decision-making process is hidden or inscrutable — cannot be easily assessed for problems like bias or model drift.
• Users could interact with the apps in a human-like way, but only My Life Story passed the Turing test, meaning a person interacting with the system couldn’t tell if it was human or not.

In less complex cases, such as providing general medical information or offering initial triage, chatbots can save time for both patients and medical professionals. By tending to routine inquiries, chatbots can free up doctors’ schedules, allowing them to focus on patients with more urgent needs or complex conditions. This synergy between chatbots and doctors could lead to improved healthcare delivery overall. With mental health ChatGPT App gaining recognition as an essential aspect of overall well-being, Woebot was developed as a mental health chatbot. This conversational agent utilizes cognitive-behavioral therapy techniques to provide users with emotional support, coping strategies, and self-care guidance. The user-friendly interface integrates smoothly with popular messaging platforms, making mental health assistance accessible and non-intimidating.

Managing health system operations and revenue cycle concerns are at the heart of how healthcare is delivered in the US. Optimizing workflows and monitoring capacity can have major implications for a healthcare organization’s bottom line and its ability to provide high-quality care. One approach to achieve this involves integrating genomic data into EHRs, which can help providers access and evaluate a more complete picture of a patient’s health. These tools are also useful in the data-gathering systems for complex drug manufacturing, and models to identify novel drug targets are reducing the time and resource investment required for drug discovery.

The benefit from increased basic information likely outweighs the risk of prompting suboptimal bystander actions. Notably, the AI performed worse in the chapters of broader interest (BLS and ALS) than in the more specialised chapters. Healthcare chatbots are not yet widely available in all languages or accessible to individuals with hearing, speech, or visual impairments. The limited availability of chatbots in various languages acts as a barrier when it comes to reaching diverse populations who could greatly benefit from their services.

In the realm of AI-driven communication, a fundamental challenge revolves around elucidating the models’ decision-making processes, a challenge often denoted as the “black box” problem (25). The complex nature of these systems frequently shrouds the rationale behind their decisions, presenting a substantial barrier to cultivating trust in their application. Last year, UNC Health piloted an internal generative AI chatbot tool with a small group of clinicians and administrators to enable staff to spend more time with patients and less time in front of a computer.

For example, physicians may enter a transcript of a patient-physician encounter into a chatbot, which can then produce medical notes in seconds. To address some of the concerns around the use of AI in healthcare, experts from the Brookings Institution recommend that stakeholders focus on transparency, informed consent protections and breaking up data monopolies via health information exchanges (HIEs). Many underscore that as AI becomes more complex, black box models may become unavoidable in spite of explainability efforts. An advanced ChatGPT algorithm capable of processing vast amounts of data will remain inscrutable to humans due to its complexity. Issues like bias and trust are at the forefront of conversations about how to safely implement AI in healthcare, and these issues are particularly relevant for tools as complex as generative AI. In a recent Healthcare Strategies episode, leadership from Stanford Medicine Children’s Health detailed how investigators explore using an LLM to prevent data disclosures for adolescent patients and protect pediatric health information.