https://www.jmir.org/issue/feedJournal of Medical Internet Research2023-01-03T13:00:05-05:00JMIR Publicationseditor@jmir.orgOpen Journal Systems The leading peer-reviewed journal for digital medicine and health and health care in the internet age. https://www.jmir.org/2024/1/e50882/ Quality and Dependability of ChatGPT and DingXiangYuan Forums for Remote Orthopedic Consultations: Comparative Analysis2024-03-14T10:45:04-04:00Zhaowen XueYiming ZhangWenyi GanHuajun WangGuorong SheXiaofei Zheng<strong>Background:</strong> The widespread use of artificial intelligence, such as ChatGPT (OpenAI), is transforming sectors, including health care, while separate advancements of the internet have enabled platforms such as China’s DingXiangYuan to offer remote medical services. <strong>Objective:</strong> This study evaluates ChatGPT-4’s responses against those of professional health care providers in telemedicine, assessing artificial intelligence’s capability to support the surge in remote medical consultations and its impact on health care delivery. <strong>Methods:</strong> We sourced remote orthopedic consultations from “Doctor DingXiang,” with responses from its certified physicians as the control and ChatGPT’s responses as the experimental group. In all, 3 blindfolded, experienced orthopedic surgeons assessed responses against 7 criteria: “logical reasoning,” “internal information,” “external information,” “guiding function,” “therapeutic effect,” “medical knowledge popularization education,” and “overall satisfaction.” We used Fleiss κ to measure agreement among multiple raters. <strong>Results:</strong> Initially, consultation records for a cumulative count of 8 maladies (equivalent to 800 cases) were gathered. We ultimately included 73 consultation records by May 2023, following primary and rescreening, in which no communication records containing private information, images, or voice messages were transmitted. After statistical scoring, we discovered that ChatGPT’s “internal information” score (mean 4.61, SD 0.52 points vs mean 4.66, SD 0.49 points; <i>P</i>=.43) and “therapeutic effect” score (mean 4.43, SD 0.75 points vs mean 4.55, SD 0.62 points; <i>P</i>=.32) were lower than those of the control group, but the differences were not statistically significant. ChatGPT showed better performance with a higher “logical reasoning” score (mean 4.81, SD 0.36 points vs mean 4.75, SD 0.39 points; <i>P</i>=.38), “external information” score (mean 4.06, SD 0.72 points vs mean 3.92, SD 0.77 points; <i>P</i>=.25), and “guiding function” score (mean 4.73, SD 0.51 points vs mean 4.72, SD 0.54 points; <i>P</i>=.96), although the differences were not statistically significant. Meanwhile, the “medical knowledge popularization education” score of ChatGPT was better than that of the control group (mean 4.49, SD 0.67 points vs mean 3.87, SD 1.01 points; <i>P</i><.001), and the difference was statistically significant. In terms of “overall satisfaction,” the difference was not statistically significant between the groups (mean 8.35, SD 1.38 points vs mean 8.37, SD 1.24 points; <i>P</i>=.92). According to how Fleiss κ values were interpreted, 6 of the control group’s score points were classified as displaying “fair agreement” (<i>P</i><.001), and 1 was classified as showing “substantial agreement” (<i>P</i><.001). In the experimental group, 3 points were classified as indicating “fair agreement,” while 4 suggested “moderate agreement” (<i>P</i><.001). <strong>Conclusions:</strong> ChatGPT-4 matches the expertise found in DingXiangYuan forums’ paid consultations, excelling particularly in scientific education. It presents a promising alternative for remote health advice. For health care professionals, it could act as an aid in patient education, while patients may use it as a convenient tool for health inquiries. 2024-03-14T10:45:04-04:00 https://www.jmir.org/2024/1/e42904/ Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study2024-03-13T11:00:04-04:00Alisa Maria Vittoria ReiterJean Tori PantelMagdalena DanyelDenise HornClaus-Eric OttMartin Atta Mensah<strong>Background:</strong> While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism. <strong>Objective:</strong> We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt. <strong>Methods:</strong> Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity. <strong>Results:</strong> DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score’s levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores. <strong>Conclusions:</strong> If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face. 2024-03-13T11:00:04-04:00 https://www.jmir.org/2024/1/e50741/ Effects of a Social Media Intervention on Vaping Intentions: Randomized Dose-Response Experiment2024-03-12T10:30:03-04:00William Douglas EvansJeffrey BingenheimerJennifer CantrellJennifer KreslakeShreya TulsianiMegumi IchimiyaAlexander P D'EsterreRaquel GerardMadeline MartinElizabeth C Hair<strong>Background:</strong> e-Cigarette use, especially by young adults, is at unacceptably high levels and represents a public health risk factor. Digital media are increasingly being used to deliver antivaping campaigns, but little is known about their effectiveness or the dose-response effects of content delivery. <strong>Objective:</strong> The objectives of this study were to evaluate (1) the effectiveness of a 60-day antivaping social media intervention in changing vaping use intentions and beliefs related to the stimulus content and (2) the dose-response effects of varying levels of exposure to the intervention on vaping outcomes, including anti-industry beliefs, vaping intentions, and other attitudes and beliefs related to vaping. <strong>Methods:</strong> Participants were adults aged 18 to 24 years in the United States. They were recruited into the study through Facebook (Meta Platforms) and Instagram (Meta Platforms), completed a baseline survey, and then randomized to 1 of the 5 conditions: 0 (control), 4, 8, 16, and 32 exposures over a 15-day period between each survey wave. Follow-up data were collected 30 and 60 days after randomization. We conducted stratified analyses of the full sample and in subsamples defined by the baseline vaping status (never, former, and current). Stimulus was delivered through Facebook and Instagram in four 15-second social media videos focused on anti-industry beliefs about vaping. The main outcome measures reported in this study were self-reported exposure to social media intervention content, attitudes and beliefs about vaping, and vaping intentions. We estimated a series of multivariate linear regressions in Stata 17 (StataCorp). To capture the dose-response effect, we assigned each study arm a numerical value corresponding to the number of advertisements (exposures) delivered to participants in each arm and used this number as our focal independent variable. In each model, the predictor was the treatment arm to which each participant was assigned. <strong>Results:</strong> The baseline sample consisted of 1491 participants, and the final analysis sample consisted of 57.28% (854/1491) of the participants retained at the 60-day follow-up. We compared the retained participants with those lost to follow-up and found no statistically significant differences across demographic variables. We found a significant effect of the social media treatment on vaping intentions (β=−0.138, 95% CI −0.266 to −0.010; <i>P</i>=.04) and anti-industry beliefs (β=−0.122, 95% CI 0.008-0.237; <i>P</i>=.04) targeted by the intervention content among current vapers but not among the full sample or other strata. We found no significant effects of self-reported exposure to the stimulus. <strong>Conclusions:</strong> Social media interventions are a promising approach to preventing vaping among young adults. More research is needed on how to optimize the dosage of such interventions and the extent to which long-term exposure may affect vaping use over time. <strong>Trial Registration:</strong> ClinicalTrials.gov NCT04867668; https://clinicaltrials.gov/study/NCT04867668 2024-03-12T10:30:03-04:00 https://www.jmir.org/2024/1/e46713/ Effect of Negative Online Reviews and Physician Responses on Health Consumers’ Choice: Experimental Study2024-03-12T10:15:04-04:00Xi HanYongxi LinWenting HanKe LiaoKefu Mei<strong>Background:</strong> The COVID-19 pandemic has highlighted the importance of online medical services. Although some researchers have investigated how numerical ratings affect consumer choice, limited studies have focused on the effect of negative reviews that most concern physicians. <strong>Objective:</strong> This study aimed to investigate how negative review features, including proportion (low/high), claim type (evaluative/factual), and physician response (absence/presence), influence consumers’ physician evaluation process under conditions in which a physician’s overall rating is high. <strong>Methods:</strong> Using a 2×2×2 between-subject decision-controlled experiment, this study examined participants’ judgment on physicians with different textual reviews. Collected data were analyzed using the t test and partial least squares–structural equation modeling. <strong>Results:</strong> Negative reviews decreased consumers’ physician selection intention. The negative review proportion (β=–0.371, <i>P</i><.001) and claim type (β=–0.343, <i>P</i><.001) had a greater effect on consumers’ physician selection intention compared to the physician response (β=0.194, <i>P</i><.001). A high negative review proportion, factual negative reviews, and the absence of a physician response significantly reduced consumers’ physician selection intention compared to their counterparts. Consumers’ locus attributions on the negative reviews affected their evaluation process. Physician attribution mediated the effects of review proportion (β=–0.150, <i>P</i><.001), review claim type (β=–0.068, <i>P</i>=.01), and physician response (β=0.167, <i>P</i><.001) on consumer choice. Reviewer attribution also mediated the effects of review proportion (β=–0.071, <i>P</i><.001), review claim type (β=–0.025, <i>P</i>=.01), and physician response (β=0.096, <i>P</i><.001) on consumer choice. The moderating effects of the physician response on the relationship between review proportion and physician attribution (β=–0.185, <i>P</i><.001), review proportion and reviewer attribution (β=–0.110, <i>P</i><.001), claim type and physician attribution (β=–0.123, <i>P</i>=.003), and claim type and reviewer attribution (β=–0.074, <i>P</i>=.04) were all significant. <strong>Conclusions:</strong> Negative review features and the physician response significantly influence consumer choice through the causal attribution to physicians and reviewers. Physician attribution has a greater effect on consumers’ physician selection intention than reviewer attribution does. The presence of a physician response decreases the influence of negative reviews through direct and moderating effects. We propose some practical implications for physicians, health care providers, and online medical service platforms. 2024-03-12T10:15:04-04:00 https://www.jmir.org/2024/1/e48977/ Online Health Information Seeking and Preventative Health Actions: Cross-Generational Online Survey Study2024-03-11T10:15:04-04:00Jayati SinhaNuket Serin<strong>Background:</strong> The popularity of online health information seeking (OHIS) has increased significantly owing to its accessibility and affordability. To facilitate better health management, it is essential to comprehend the generational differences in OHIS behavior and preventative health actions after seeking online health information (OHI). <strong>Objective:</strong> This study investigates the variations in OHIS and engagement in preventative health actions between 2 generations based on their technology use (digital natives [aged 18-42 years] and digital immigrants [aged ≥43 years]). Additionally, this research explores the mediating role of OHIS types on the generational effect on preventative health actions and the moderating role of OHI search frequency, gender, and the presence of chronic diseases on the generational effect on OHIS types and preventative health actions. <strong>Methods:</strong> A preregistered online survey was conducted on the Prolific online data collection platform using stratified sampling of 2 generations (digital natives and digital immigrants) from the United States in November 2023. Overall, 3 types of OHIS were collected: health wellness information search, health guidance information search, and health management information search. A 1-way analysis of covariance tested the generational differences in types of OHIS and preventative health actions, and a 2-way analysis of covariance tested the moderating role of OHIS search frequency, gender, and the presence of chronic diseases using 7 control variables. The PROCESS Macro Model 4 was used to conduct mediation analyses, testing OHI search types as mediators. Linear regression analyses tested age as a predictor of OHIS and preventative health actions. <strong>Results:</strong> The analysis of 1137 responses revealed generational differences in OHIS. Digital natives searched for health wellness information more frequently (<i>P</i><.001), whereas digital immigrants searched for health guidance (<i>P</i><.001) and health management information (<i>P</i>=.001) more frequently. There were no significant differences between the 2 generations regarding preventative health actions (<i>P</i>=.85). Moreover, all 3 types of OHIS mediated the relationship between generational differences and preventative health actions. Furthermore, as people aged, they searched for significantly less health wellness information (<i>P</i><.001) and more health guidance (<i>P</i><.001), and health management information (<i>P</i>=.003). Age was not a significant predictor of preventative health actions (<i>P</i>=.48). The frequency of OHI searches did not moderate the effect of generations on OHIS types and preventative health actions. Gender only moderated the relationship between generation and health guidance information search (<i>P</i>=.02), and chronic diseases only moderated the relationship between generation and health wellness information search (<i>P</i>=.03). <strong>Conclusions:</strong> To the best of our knowledge, this study is the first to explore how 2 digital generations vary in terms of searching for OHI and preventative health behaviors. As the older adult population grows, it is crucial to understand their OHIS behavior and how they engage in preventative health actions to enhance their quality of life. <strong>Trial Registration:</strong> 2024-03-11T10:15:04-04:00 https://www.jmir.org/2024/1/e47715/ The Impact of Digital Hospitals on Patient and Clinician Experience: Systematic Review and Qualitative Evidence Synthesis2024-03-11T10:00:05-04:00Oliver J CanfellLeanna WoodsYasaman MeshkatJenna KrivitBrinda GunashanharChristine SladeAndrew Burton-JonesClair Sullivan<strong>Background:</strong> The digital transformation of health care is advancing rapidly. A well-accepted framework for health care improvement is the Quadruple Aim: improved clinician experience, improved patient experience, improved population health, and reduced health care costs. Hospitals are attempting to improve care by using digital technologies, but the effectiveness of these technologies is often only measured against cost and quality indicators, and less is known about the clinician and patient experience. <strong>Objective:</strong> This study aims to conduct a systematic review and qualitative evidence synthesis to assess the clinician and patient experience of digital hospitals. <strong>Methods:</strong> The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) guidelines were followed. The PubMed, Embase, Scopus, CINAHL, and PsycINFO databases were searched from January 2010 to June 2022. Studies that explored multidisciplinary clinician or adult inpatient experiences of digital hospitals (with a full electronic medical record) were included. Study quality was assessed using the Mixed Methods Appraisal Tool. Data synthesis was performed narratively for quantitative studies. Qualitative evidence synthesis was performed via (1) automated machine learning text analytics using Leximancer (Leximancer Pty Ltd) and (2) researcher-led inductive synthesis to generate themes. <strong>Results:</strong> A total of 61 studies (n=39, 64% quantitative; n=15, 25% qualitative; and n=7, 11% mixed methods) were included. Most studies (55/61, 90%) investigated clinician experiences, whereas few (10/61, 16%) investigated patient experiences. The study populations ranged from 8 to 3610 clinicians, 11 to 34,425 patients, and 5 to 2836 hospitals. Quantitative outcomes indicated that clinicians had a positive overall satisfaction (17/24, 71% of the studies) with digital hospitals, and most studies (11/19, 58%) reported a positive sentiment toward usability. Data accessibility was reported positively, whereas adaptation, clinician-patient interaction, and workload burnout were reported negatively. The effects of digital hospitals on patient safety and clinicians’ ability to deliver patient care were mixed. The qualitative evidence synthesis of clinician experience studies (18/61, 30%) generated 7 themes: inefficient digital documentation, inconsistent data quality, disruptions to conventional health care relationships, acceptance, safety versus risk, reliance on hybrid (digital and paper) workflows, and patient data privacy. There was weak evidence of a positive association between digital hospitals and patient satisfaction scores. <strong>Conclusions:</strong> Clinicians’ experience of digital hospitals appears positive according to high-level indicators (eg, overall satisfaction and data accessibility), but the qualitative evidence synthesis revealed substantive tensions. There is insufficient evidence to draw a definitive conclusion on the patient experience within digital hospitals, but indications appear positive or agnostic. Future research must prioritize equitable investigation and definition of the digital clinician and patient experience to achieve the Quadruple Aim of health care. 2024-03-11T10:00:05-04:00 https://www.jmir.org/2024/1/e47448/ Willingness to Use Digital Health Screening and Tracking Tools for Public Health in Sexual Minority Populations in a National Probability Sample: Quantitative Intersectional Analysis2024-03-08T13:30:04-05:00Wilson Vincent<strong>Background:</strong> Little is known about sexual minority adults’ willingness to use digital health tools, such as pandemic-related tools for screening and tracking, outside of HIV prevention and intervention efforts for sexual minority men, specifically. Additionally, given the current cultural climate in the United States, heterosexual and sexual minority adults may differ in their willingness to use digital health tools, and there may be within-group differences among sexual minority adults. <strong>Objective:</strong> This study compared sexual minority and heterosexual adults’ willingness to use COVID-19–related digital health tools for public health screening and tracking and tested whether sexual minority adults differed from each other by age group, gender, and race or ethnicity. <strong>Methods:</strong> We analyzed data from a cross-sectional, national probability survey (n=2047) implemented from May 30 to June 8, 2020, in the United States during the height of the public health response to the COVID-19 pandemic. Using latent-variable modeling, heterosexual and sexual minority adults were tested for differences in their willingness to use digital health tools for public health screening and tracking. Among sexual minority adults, specifically, associations with age, gender, and race or ethnicity were assessed. <strong>Results:</strong> On average, sexual minority adults showed greater willingness to use digital health tools for screening and tracking than heterosexual adults (latent factor mean difference 0.46, 95% CI 0.15-0.77). Among sexual minority adults, there were no differences by age group, gender, or race or ethnicity. However, African American (<i>b</i>=0.41, 95% CI 0.19-0.62), Hispanic or Latino (<i>b</i>=0.36, 95% CI 0.18-0.55), and other racial or ethnic minority (<i>b</i>=0.54, 95% CI 0.31-0.77) heterosexual adults showed greater willingness to use digital health tools for screening and tracking than White heterosexual adults. <strong>Conclusions:</strong> In the United States, sexual minority adults were more willing to use digital health tools for screening and tracking than heterosexual adults. Sexual minority adults did not differ from each other by age, gender, or race or ethnicity in terms of their willingness to use these digital health tools, so no sexual orientation-based or intersectional disparities were identified. Furthermore, White heterosexual adults were less willing to use these tools than racial or ethnic minority heterosexual adults. Findings support the use of digital health tools with sexual minority adults, which could be important for other public health-related concerns (eg, the recent example of mpox). Additional studies are needed regarding the decision-making process of White heterosexual adults regarding the use of digital health tools to address public health crises, including pandemics or outbreaks that disproportionately affect minoritized populations. <strong>Trial Registration:</strong> 2024-03-08T13:30:04-05:00 https://www.jmir.org/2024/1/e50278/ Dental Students’ Satisfaction With Web-Based Learning During the Initial Phase of the COVID-19 Pandemic: Mixed Methods Study2024-03-08T12:00:05-05:00Minjung LeeSo Youn AnJungjoon Ihm<strong>Background:</strong> The COVID-19 pandemic has precipitated an accelerated shift in education, moving from traditional learning to web-based learning. This transition introduced a notable transactional distance (TD) between the instructors and learners. Although disease control and staff and students’ safety are the top priorities during a pandemic, the successful delivery of education is equally crucial. However, the ramifications of this swift transition are particularly critical in the context of dental education. Dental education is inherently practice oriented, necessitating hands-on training and manual skills development, which poses unique challenges to distance learning approaches. <strong>Objective:</strong> This study aims to examine dental students’ web-based learning satisfaction and experience of TD, investigate the predictors of web-based learning satisfaction, and explore the perceptions of students about the advantages and disadvantages of web-based learning. <strong>Methods:</strong> This study explored the factors associated with web-based learning satisfaction using TD theory during the transition to web-based education. Psychological factors that could influence satisfaction were adapted from the health belief model. We conducted a cross-sectional web-based survey of 345 dental students from 2 institutions in South Korea who were enrolled in the spring semester of 2020. Data were collected between July 8 and September 14, 2020. Qualitative analysis was used to examine responses to open-ended questions concerning perceptions of web-based learning. <strong>Results:</strong> A multivariate hierarchical linear regression model was used to analyze the effects of student characteristics, TD, and psychological factors (ie, perceived risk of infection and efficacy belief of web-based learning) on web-based learning satisfaction. The average score for web-based learning satisfaction was 3.62 (SD 0.84), just above the midpoint of the possible range (1-5). Self-regulated learning (β=0.08; <i>P</i>=.046), learner-instructor interaction (β=0.08; <i>P</i>=.03), and learner-content interaction (β=0.64; <i>P</i><.001) were associated with higher levels of satisfaction. Moreover, a significant association was revealed between high efficacy beliefs in web-based learning (β=0.20; <i>P</i><.001) and satisfaction. Although the learning structure (synchronous vs asynchronous) did not exhibit a significant association with satisfaction, the qualitative analysis results revealed that each structure possesses distinct strengths and weaknesses. The students in synchronous learning (79/345, 22.9%) recognized heightened autonomy in the “learning environment” (19/79, 24%); however, technical issues (28/79, 35%) and reduced concentration (15/79, 19%) were identified as downsides. Conversely, the students in asynchronous settings (266/345, 77.1%) emphasized unlimited access to learning content (74/266, 27.8%) and the flexibility of “learning in preferred time” (69/266, 25.9%). Nevertheless, challenges, such as self-management difficulties (66/266, 24.8%) and limited interactions (55/266, 20.7%), were evident. <strong>Conclusions:</strong> The findings suggest that efforts to minimize TD, facilitating self-regulated learning and interaction among students and instructors, are critical for achieving web-based learning satisfaction. Moreover, establishing a common understanding among students regarding the necessity and efficacy of web-based learning during epidemics could enhance their satisfaction. <strong>Trial Registration:</strong> 2024-03-08T12:00:05-05:00 https://www.jmir.org/2024/1/e54107/ Generation Z’s Health Information Avoidance Behavior: Insights From Focus Group Discussions2024-03-08T11:15:03-05:00Chenjin JiaPengcheng Li<strong>Background:</strong> Younger generations actively use social media to access health information. However, research shows that they also avoid obtaining health information online at times when confronted with uncertainty. <strong>Objective:</strong> This study aims to examine the phenomenon of health information avoidance among Generation Z, a representative cohort of active web users in this era. <strong>Methods:</strong> Drawing on the planned risk information avoidance model, we adopted a qualitative approach to explore the factors related to information avoidance within the context of health and risk communication. The researchers recruited 38 participants aged 16 to 25 years for the focus group discussion sessions. <strong>Results:</strong> In this study, we sought to perform a deductive qualitative analysis of the focus group interview content with open, focused, and theoretical coding. Our findings support several key components of the planned risk information avoidance model while highlighting the underlying influence of cognition on emotions. Specifically, socioculturally, group identity and social norms among peers lead some to avoid health information. Cognitively, mixed levels of risk perception, conflicting values, information overload, and low credibility of information sources elicited their information avoidance behaviors. Affectively, negative emotions such as anxiety, frustration, and the desire to stay positive contributed to avoidance. <strong>Conclusions:</strong> This study has implications for understanding young users’ information avoidance behaviors in both academia and practice. 2024-03-08T11:15:03-05:00 https://www.jmir.org/2024/1/e53008/ Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges2024-03-08T10:45:04-05:00Yan ChenPouyan EsmaeilzadehAs advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data-intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support, while identifying security and privacy threats within each phase of the life cycle of such systems (ie, data collection, model development, and implementation phases). The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks. This study highlights the importance of addressing the security and privacy threats associated with generative AI in health care to ensure the safe and effective use of these systems. The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks associated with these systems. By examining the use cases and benefits of generative AI across diverse domains within health care, this paper contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. In addition, this study provides practical insights for stakeholders looking to adopt generative AI solutions within their organizations.2024-03-08T10:45:04-05:00