BACKGROUND: Patients find technology tools to be more approachable for seeking sensitive health-related information, such as reproductive health information. The inventive conversational ability of artificial intelligence (AI) chatbots, such as ChatGPT (OpenAI Inc), offers a potential means for patients to effectively locate answers to their health-related questions digitally.
OBJECTIVE: A pilot study was conducted to compare the novel ChatGPT with the existing Google Search technology for their ability to offer accurate, effective, and current information regarding proceeding action after missing a dose of oral contraceptive pill.
METHODS: A sequence of 11 questions, mimicking a patient inquiring about the action to take after missing a dose of an oral contraceptive pill, were input into ChatGPT as a cascade, given the conversational ability of ChatGPT. The questions were input into 4 different ChatGPT accounts, with the account holders being of various demographics, to evaluate potential differences and biases in the responses given to different account holders. The leading question, \"what should I do if I missed a day of my oral contraception birth control?\" alone was then input into Google Search, given its nonconversational nature. The results from the ChatGPT questions and the Google Search results for the leading question were evaluated on their readability, accuracy, and effective delivery of information.
RESULTS: The ChatGPT results were determined to be at an overall higher-grade reading level, with a longer reading duration, less accurate, less current, and with a less effective delivery of information. In contrast, the Google Search resulting answer box and snippets were at a lower-grade reading level, shorter reading duration, more current, able to reference the origin of the information (transparent), and provided the information in various formats in addition to text.
CONCLUSIONS: ChatGPT has room for improvement in accuracy, transparency, recency, and reliability before it can equitably be implemented into health care information delivery and provide the potential benefits it poses. However, AI may be used as a tool for providers to educate their patients in preferred, creative, and efficient ways, such as using AI to generate accessible short educational videos from health care provider-vetted information. Larger studies representing a diverse group of users are needed.

Artificial intelligence use is increasing exponentially, including by patients in medical decision- making. Because of the limitations of chatbots and the possibility of receiving erroneous or incomplete information, patient.

BACKGROUND: Mobile health (mHealth) apps offer unique opportunities to support self-care and behavior change, but poor user engagement limits their effectiveness. This is particularly true for fully automated mHealth apps without any human support. Human support in mHealth apps is associated with better engagement but at the cost of reduced scalability.
OBJECTIVE: This work aimed to (1) describe the theory-informed development of a fully automated relaxation and mindfulness app to reduce distress in people with cancer (CanRelax app 2.0), (2) describe engagement with the app on multiple levels within a fully automated randomized controlled trial over 10 weeks, and (3) examine whether engagement was related to user characteristics.
METHODS: The CanRelax app 2.0 was developed in iterative processes involving input from people with cancer and relevant experts. The app includes evidence-based relaxation exercises, personalized weekly coaching sessions with a rule-based conversational agent, 39 self-enactable behavior change techniques, a self-monitoring dashboard with gamification elements, highly tailored reminder notifications, an educational video clip, and personalized in-app letters. For the larger study, German-speaking adults diagnosed with cancer within the last 5 years were recruited via the web in Switzerland, Austria, and Germany. Engagement was analyzed in a sample of 100 study participants with multiple measures on a micro level (completed coaching sessions, relaxation exercises practiced with the app, and feedback on the app) and a macro level (relaxation exercises practiced without the app and self-efficacy toward self-set weekly relaxation goals).
RESULTS: In week 10, a total of 62% (62/100) of the participants were actively using the CanRelax app 2.0. No associations were identified between engagement and level of distress at baseline, sex assigned at birth, educational attainment, or age. At the micro level, 71.88% (3520/4897) of all relaxation exercises and 714 coaching sessions were completed in the app, and all participants who provided feedback (52/100, 52%) expressed positive app experiences. At the macro level, 28.12% (1377/4897) of relaxation exercises were completed without the app, and participants\' self-efficacy remained stable at a high level. At the same time, participants raised their weekly relaxation goals, which indicates a potential relative increase in self-efficacy.
CONCLUSIONS: The CanRelax app 2.0 achieved promising engagement even though it provided no human support. Fully automated social components might have compensated for the lack of human involvement and should be investigated further. More than one-quarter (1377/4897, 28.12%) of all relaxation exercises were practiced without the app, highlighting the importance of assessing engagement on multiple levels.

BACKGROUND: Large language models (LLMs), such as ChatGPT (Open AI), are increasingly used in medicine and supplement standard search engines as information sources. This leads to more \"consultations\" of LLMs about personal medical symptoms.
OBJECTIVE: This study aims to evaluate ChatGPT\'s performance in answering clinical case-based questions in otorhinolaryngology (ORL) in comparison to ORL consultants\' answers.
METHODS: We used 41 case-based questions from established ORL study books and past German state examinations for doctors. The questions were answered by both ORL consultants and ChatGPT 3. ORL consultants rated all responses, except their own, on medical adequacy, conciseness, coherence, and comprehensibility using a 6-point Likert scale. They also identified (in a blinded setting) if the answer was created by an ORL consultant or ChatGPT. Additionally, the character count was compared. Due to the rapidly evolving pace of technology, a comparison between responses generated by ChatGPT 3 and ChatGPT 4 was included to give an insight into the evolving potential of LLMs.
RESULTS: Ratings in all categories were significantly higher for ORL consultants (P<.001). Although inferior to the scores of the ORL consultants, ChatGPT\'s scores were relatively higher in semantic categories (conciseness, coherence, and comprehensibility) compared to medical adequacy. ORL consultants identified ChatGPT as the source correctly in 98.4% (121/123) of cases. ChatGPT\'s answers had a significantly higher character count compared to ORL consultants (P<.001). Comparison between responses generated by ChatGPT 3 and ChatGPT 4 showed a slight improvement in medical accuracy as well as a better coherence of the answers provided. Contrarily, neither the conciseness (P=.06) nor the comprehensibility (P=.08) improved significantly despite the significant increase in the mean amount of characters by 52.5% (n= (1470-964)/964; P<.001).
CONCLUSIONS: While ChatGPT provided longer answers to medical problems, medical adequacy and conciseness were significantly lower compared to ORL consultants\' answers. LLMs have potential as augmentative tools for medical care, but their \"consultation\" for medical problems carries a high risk of misinformation as their high semantic quality may mask contextual deficits.

BACKGROUND: ChatGPT has gained global attention recently owing to its high performance in generating a wide range of information and retrieving any kind of data instantaneously. ChatGPT has also been tested for the United States Medical Licensing Examination (USMLE) and has successfully cleared it. Thus, its usability in medical education is now one of the key discussions worldwide.
OBJECTIVE: The objective of this study is to evaluate the performance of ChatGPT in medical biochemistry using clinical case vignettes.
METHODS: The performance of ChatGPT was evaluated in medical biochemistry using 10 clinical case vignettes. Clinical case vignettes were randomly selected and inputted in ChatGPT along with the response options. We tested the responses for each clinical case twice. The answers generated by ChatGPT were saved and checked using our reference material.
RESULTS: ChatGPT generated correct answers for 4 questions on the first attempt. For the other cases, there were differences in responses generated by ChatGPT in the first and second attempts. In the second attempt, ChatGPT provided correct answers for 6 questions and incorrect answers for 4 questions out of the 10 cases that were used. But, to our surprise, for case 3, different answers were obtained with multiple attempts. We believe this to have happened owing to the complexity of the case, which involved addressing various critical medical aspects related to amino acid metabolism in a balanced approach.
CONCLUSIONS: According to the findings of our study, ChatGPT may not be considered an accurate information provider for application in medical education to improve learning and assessment. However, our study was limited by a small sample size (10 clinical case vignettes) and the use of the publicly available version of ChatGPT (version 3.5). Although artificial intelligence (AI) has the capability to transform medical education, we emphasize the validation of such data produced by such AI systems for correctness and dependability before it could be implemented in practice.

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BACKGROUND: With the increasing integration of artificial intelligence (AI) in health care, AI chatbots like ChatGPT-4 are being used to deliver health information.
OBJECTIVE: This study aimed to assess the capability of ChatGPT-4 in answering common questions related to abdominoplasty, evaluating its potential as an adjunctive tool in patient education and preoperative consultation.
METHODS: A variety of common questions about abdominoplasty were submitted to ChatGPT-4. These questions were sourced from a question list provided by the American Society of Plastic Surgery to ensure their relevance and comprehensiveness. An experienced plastic surgeon meticulously evaluated the responses generated by ChatGPT-4 in terms of informational depth, response articulation, and competency to determine the proficiency of the AI in providing patient-centered information.
RESULTS: The study showed that ChatGPT-4 can give clear answers, making it useful for answering common queries. However, it struggled with personalized advice and sometimes provided incorrect or outdated references. Overall, ChatGPT-4 can effectively share abdominoplasty information, which may help patients better understand the procedure. Despite these positive findings, the AI needs more refinement, especially in providing personalized and accurate information, to fully meet patient education needs in plastic surgery.
CONCLUSIONS: Although ChatGPT-4 shows promise as a resource for patient education, continuous improvements and rigorous checks are essential for its beneficial integration into healthcare settings. The study emphasizes the need for further research, particularly focused on improving the personalization and accuracy of AI responses.
METHODS: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

We evaluated whether the artificial intelligence chatbot ChatGPT can adequately answer patient questions related to [18F]FDG PET/CT in common clinical indications before and after scanning. Methods: Thirteen questions regarding [18F]FDG PET/CT were submitted to ChatGPT. ChatGPT was also asked to explain 6 PET/CT reports (lung cancer, Hodgkin lymphoma) and answer 6 follow-up questions (e.g., on tumor stage or recommended treatment). To be rated \"useful\" or \"appropriate,\" a response had to be adequate by the standards of the nuclear medicine staff. Inconsistency was assessed by regenerating responses. Results: Responses were rated \"appropriate\" for 92% of 25 tasks and \"useful\" for 96%. Considerable inconsistencies were found between regenerated responses for 16% of tasks. Responses to 83% of sensitive questions (e.g., staging/treatment options) were rated \"empathetic.\" Conclusion: ChatGPT might adequately substitute for advice given to patients by nuclear medicine staff in the investigated settings. Improving the consistency of ChatGPT would further increase reliability.

BACKGROUND: The prevalence of child and adolescent mental health issues is increasing faster than the number of services available, leading to a shortfall. Mental health chatbots are a highly scalable method to address this gap. Manage Your Life Online (MYLO) is an artificially intelligent chatbot that emulates the method of levels therapy. Method of levels is a therapy that uses curious questioning to support the sustained awareness and exploration of current problems.
OBJECTIVE: This study aimed to assess the feasibility and acceptability of a co-designed interface for MYLO in young people aged 16 to 24 years with mental health problems.
METHODS: An iterative co-design phase occurred over 4 months, in which feedback was elicited from a group of young people (n=7) with lived experiences of mental health issues. This resulted in the development of a progressive web application version of MYLO that could be used on mobile phones. We conducted a case series to assess the feasibility and acceptability of MYLO in 13 young people over 2 weeks. During this time, the participants tested MYLO and completed surveys including clinical outcomes and acceptability measures. We then conducted focus groups and interviews and used thematic analysis to obtain feedback on MYLO and identify recommendations for further improvements.
RESULTS: Most participants were positive about their experience of using MYLO and would recommend MYLO to others. The participants enjoyed the simplicity of the interface, found it easy to use, and rated it as acceptable using the System Usability Scale. Inspection of the use data found evidence that MYLO can learn and adapt its questioning in response to user input. We found a large effect size for the decrease in participants\' problem-related distress and a medium effect size for the increase in their self-reported tendency to resolve goal conflicts (the proposed mechanism of change) in the testing phase. Some patients also experienced a reliable change in their clinical outcome measures over the 2 weeks.
CONCLUSIONS: We established the feasibility and acceptability of MYLO. The initial outcomes suggest that MYLO has the potential to support the mental health of young people and help them resolve their own problems. We aim to establish whether the use of MYLO leads to a meaningful reduction in participants\' symptoms of depression and anxiety and whether these are maintained over time by conducting a randomized controlled evaluation trial.

The world is moving fast toward digital transformation as we live in the artificial intelligence (AI) era. The COVID-19 pandemic accelerates this movement. Chatbots were used successfully to help researchers collect data for research purposes.
To implement a chatbot on the Facebook platform to establish connections with health care professionals who had subscribed to the chatbot, provide medical and pharmaceutical educational content, and collect data for online pharmacy research projects. Facebook was chosen because it has billions of daily active users, which offers a massive potential audience for research projects.
The chatbot was successfully implemented on the Facebook platform following 3 consecutive steps. Firstly, the ChatPion script was installed on the Pharmind website to establish the chatbot system. Secondly, the PharmindBot application was developed on Facebook. Finally, the PharmindBot app was integrated with the chatbot system.
The chatbot responds automatically to public comments and sends subscribers private responses using AI. The chatbot collected quantitative and qualitative data with minimal costs.
The chatbot\'s auto-reply function was tested using a post published on a specific page on Facebook. Testers were asked to leave predefined keywords to test its functionality. The chatbot\'s ability to collect and save data was tested by asking testers to fill out an online survey within Facebook Messenger for quantitative data and answer predefined questions for qualitative data.
The chatbot was tested on 1000 subscribers who interacted with it. Almost all testers (n = 990, 99%) obtained a successful private reply from the chatbot after sending a predefined keyword. Also, the chatbot replied privately to almost all public comments (n = 985, 98.5%) which helped to increase the organic reach and to establish a connection with the chatbot subscribers. No missing data were found when the chatbot was used to collect quantitative and qualitative data.
The chatbot reached thousands of health care professionals and provided them with automated responses. At a low cost, the chatbot was able to gather both qualitative and quantitative data without relying on Facebook ads to reach the intended audience. The data collection was efficient and effective. Using chatbots by pharmacy and medical researchers will help do more feasible online studies using AI to advance health care research.