Journal of Medical Internet Research

Understanding Remission of Long-Term Conditions Through Electronic Health Records: Scoping Review

2026-05-19T17:00:22-04:00

Background: Multiple long-term conditions (MLTCs) require complex and prolonged treatment regimens. Remission in long-term conditions (LTCs) is important for understanding disease progression and evaluating treatment effectiveness. Electronic health records (EHRs) are increasingly used to monitor clinical outcomes, but how remission is defined within EHRs remains unclear. Objective: This study aimed to summarize and collate the previous literature on how remission of LTCs has been defined in EHRs. Methods: Systematic electronic searches were performed on OVID MEDLINE, Embase, CINAHL EBSCO, the Cochrane Library, and the Bielefeld Academic Search Engine for eligible studies published from inception to November 27, 2025. Quantitative studies, published in any language, on adult populations, and using EHRs to assess remission of LTCs, were eligible for inclusion. Studies that did not clearly define remission and studies on cancer remission were excluded. Data were extracted from each eligible study using a structured table. Risk of bias was not assessed, in line with scoping review methodology. A narrative approach was taken to summarize and present data from the included studies. The number and characteristics of studies were described, both overall and by condition. Findings were discussed with clinicians and data experts to ensure applicability in clinical practice. Results: Ninety-one studies were included. Sample sizes ranged from 12 to 72.9 million adults. Studies were conducted in 18 countries, with the majority being from the United States. The majority of included studies used a cohort study design. Studies assessed how remission was defined in 12 LTCs, including inflammatory bowel disease (41/91, 45.1%), type 2 diabetes (n=15, 16.5%), depression (n=15, 16.5%), alcohol or drug misuse (n=8, 8.8%), asthma (n=3, 3.3%), multiple sclerosis (n=3, 3.3%), epilepsy (n=1, 1.1%), anemia (n=1, 1.1%), chronic kidney disease (n=1, 1.1%), autoimmune pancreatitis (n=1, 1.1%), hypertension (n=1, 1.1%), heart failure (n=1, 1.1%), and MLTC (n=1, 1.1%). Remission was typically defined using a combination of clinical codes (n=7, 7.7%), validated rating scales (n=56, 61.5%), biochemical markers (n=29, 31.9%), absence of symptoms (n=10, 11%), absence of condition-specific events (eg, hospital admissions; n=4, 4.4%), and cessation of pharmacological treatments (n=26, 28.6%). There was substantial variation in the criteria and duration of follow-up used to define remission across studies. Conclusions: This review demonstrates that remission of LTCs can be identified and operationalized within EHRs, although remission criteria varied across studies. The review extends the literature on remission in EHRs by combining evidence synthesis and consultation with clinical and data experts to propose standardized comprehensive definitions to reliably define and implement remission of multiple LTCs in EHR-based research. This will allow cross-study comparisons and present an opportunity to advance understanding of disease trajectories and improve evaluation and monitoring of patient outcomes. Further research may apply, compare, and evaluate standardized definitions across different data sources to assess generalizability and further improve our understanding of remission of LTCs.

Use of Health and Well-Being Technology, Basic Psychological Needs, and the Mediating Role of Technological Identity in 6 European Countries: Prospective Longitudinal Survey Study

2026-05-19T17:00:04-04:00

Background: Digital health technologies are increasingly used to monitor and improve personal health and well-being. Simultaneously, they can influence user behavior and self-understanding. As health technologies advance and are embedded in everyday life, understanding their broader psychological impacts, as well as the role of identity in shaping these outcomes, is crucial. Objective: This prospective longitudinal survey study examined how usage of health and well-being technologies predicts experiences of new technology-related basic psychological needs over time, reflecting the psychological outcomes of digital health technologies. Furthermore, we investigated whether technological social identity serves as a pathway through which health technology use is associated with basic needs. Methods: We used 3-wave survey data (2022-2024) collected from participants aged 18-75 years in Finland (n=1541), France (n=1561), Germany (n=1529), Ireland (n=1112), Italy (n=1530), and Poland (n=1533). Participants were recruited through a survey research panel, and follow-up data were collected from the same cohort. The sample was representative of the target populations by age (average age 46.79, SD 15.50 years) and gender (4304/8806, 48.88% male). The measure of digital health technology usage included smartphone health and well-being apps, well-being coaching apps, fitness trackers or watches, and smart rings. We applied a dynamic panel model within a structural equation modeling framework to examine both contemporaneous and lagged effects of technology use on the outcome variables—autonomy frustration, competence frustration, and relatedness satisfaction, measured with the Technology Effects on Need Satisfaction in Life scale. Mediation analysis was performed to assess whether social identification as a new technology user explained the relationship between technology use and need experiences. Results: Usage of health and well-being technologies was associated with higher technology-mediated relatedness (β=.14, 95% CI 0.11-0.18; P<.001). Autonomy frustration (β=.06, 95% CI 0.02-0.10; P=.003) and competence frustration (β=.06, 95% CI 0.02-0.10; P=.008) also demonstrated small but positive connections with technology use. We found no statistically significant differences across countries. Mediation analysis revealed that the relationships between technology use and psychological needs were largely explained by social identification as a technology user. Conclusions: Unlike most existing studies, this research focuses on the technology-related psychological effects of everyday health and well-being technologies and provides longitudinal and cross-national evidence of how they shape broader outcomes beyond health content and goals. The findings demonstrate that health technologies can both support users’ social needs and undermine their personal sense of agency. They also provide insights into the significance and dual-edged role of technology users’ social identity in transmitting these effects. The study highlights the central role of technology-related basic needs and identity processes in understanding the larger outcomes of rapidly advancing digital health technologies. It offers valuable insights for health technology designs and implementations that aim to enable need-supportive technology engagement. Trial Registration:

Nicotine Dependence and Quit Self-Confidence in a Smoking Cessation Program Using a Group-Based Digital Peer-Supported App and Cigarette Consumption–Adjusted Nicotine Aids Among Japanese Workers: Retrospective Cohort Study

2026-05-19T16:30:15-04:00

Background: Completion rates for smoking cessation treatments under Japan’s national health insurance system remain suboptimal. A workplace cessation program, combining nicotine gum or patches with a group-based digital peer-supported app, has reported high cessation success rates. Although nicotine dependence is generally associated with lower cessation success, and self-confidence is generally associated with higher success, these associations may differ by tobacco product type. Evidence on these relationships in app-based cessation programs remains limited. Objective: This study aimed to examine the independent and combined associations of nicotine dependence and self-confidence in quitting with smoking cessation success among cigarette-only smokers, heated tobacco product–only users, and dual users. Methods: This retrospective cohort study used data from a workplace cessation program in Japan. Participants were eligible if they were employed, owned a smartphone, and self-enrolled in the program. Recruitment was conducted through workplace promotion and individual outreach, primarily via email from companies. The program combined a digital peer-support app with nicotine gum or patches, adjusted according to cigarette consumption. The app included anonymous peer-support group chats of up to 5 participants, where participants shared progress, photos, and comments. Nicotine dependence was assessed by the time to the first cigarette after waking (high: ≤30 min; low: >30 min). Self-confidence for quitting was rated on a 0‐10 scale and dichotomized at the median. A 4-level variable combined nicotine dependence and self-confidence for quitting. Logistic regression analyses were conducted by tobacco product type, and odds ratios (ORs) and 95% CIs were estimated. Results: A total of 2143 participants were included in the analysis. Their mean age was 46.5 (SD 10.9) years, and approximately 90% were men. Overall cessation success was 53.8% (1152/2143). Participants with high self-confidence had a higher cessation success rate than those with low self-confidence (529/834, 63.4% vs 623/1309, 47.6%), with an OR of 1.81 (95% CI 1.55‐2.12). Nicotine dependence was significantly associated with cessation success only among cigarette-only smokers; those with low nicotine dependence had a higher OR than those with high dependence (OR 1.59, 95% CI 1.07‐2.37). Across all tobacco product types, the subgroup with low nicotine dependence and high self-confidence showed the highest cessation success, followed by the subgroup with high nicotine dependence but high self-confidence. Conclusions: By distinguishing cigarette-only smokers, heated tobacco product–only users, and dual users in a workplace cessation program, this study provides novel, product-specific evidence regarding nicotine dependence and self-confidence related to cessation success. This study extends prior research beyond cigarette-only smokers by examining tobacco user groups and suggests that self-confidence may predict cessation across tobacco product types, whereas the role of nicotine dependence may be product-specific. These findings may inform tailored, scalable smoking-cessation support in workplaces; however, they should be interpreted with caution because cessation outcomes were self-reported.

Structural Inequalities in Online Health Information Seeking: Cross-National Multilevel Study

2026-05-19T16:00:22-04:00

Background: Online health information–seeking behavior (OHISB) has become an increasingly common component of contemporary health self-management. Individuals use a wide range of digital sources, including websites, social media platforms, and mobile apps, to obtain health-related information. However, substantial disparities persist in who seeks health information online, and which populations benefit from digital health resources. While previous research has largely focused on individual-level determinants, cross-national evidence on structural influences remains limited. Objective: This study aims to assess between-country variation in OHISB, examine associations between individual-level characteristics and OHISB, and investigate how country-level structural conditions are associated with cross-national differences in OHISB, net of individual-level characteristics. Methods: Data were drawn from the Health and Health Care II module of the International Social Survey Programme (ISSP 2021‐2024; n=35,592; 32 countries). OHISB was measured as any use of the Internet to search for health-related information during the past 12 months. Multilevel logistic regression models were estimated. Country-level indicators were reduced using principal component analysis into 4 composite indices. Robustness checks included analyses excluding respondents without internet access and models incorporating survey weights. Results: OHISB varied substantially across countries (intraclass correlation coefficient=0.177). At the individual level, younger age, higher education, female respondents, recent health problems, doctor visits, unmet medical needs, and perceived usefulness of the internet were associated with higher odds of OHISB. At the macro level, the socioeconomic and health development showed the strongest association (odds ratio=1.52 per SD; =.003) and explained a substantial share of between-country variation. Cultural hierarchy–individualism was associated with OHISB in separate models but attenuated when adjusted for development. Cross-level interactions indicated that the gender gap and the role of perceived usefulness were more pronounced in higher-development contexts, although these findings were exploratory. Conclusions: OHISB is associated with both individual characteristics and broader structural conditions. Socioeconomic and health development appear to play a key contextual role in shaping cross-national differences in digital health engagement, highlighting the importance of addressing both individual and structural dimensions of digital health inequalities.

Co-Lifecycle Governance for Learning Medical AI: A Hybrid Convergence Framework for Adaptive Regulatory Oversight

2026-05-19T15:45:15-04:00

Artificial intelligence (AI) in health care is increasingly defined not by static algorithms but by adaptive intelligence—systems that evolve over time through interactions with data, clinicians, and clinical environments. This adaptive capacity creates a structural mismatch with regulatory frameworks built for technologies whose behavior remains static. As AI models drift, recalibrate, or degrade in real-world contexts, they dissolve the linear boundaries between design, deployment, and clinical interpretation. These temporal, epistemic, and organizational frictions expose responsibility gaps that cannot be resolved through incremental modifications to legacy oversight structures. Regulators across major jurisdictions are beginning to respond to these challenges, though with differing orientations. The United States advances mechanisms for predictable adaptation, including Predetermined Change Control Plans, real-world evidence frameworks, and life cycle–oriented quality management reforms. The European Union emphasizes precautionary, rights-based governance through the European Union Artificial Intelligence Act (AI Act) and modernized liability rules. South Korea, operating within a hyperconnected digital health ecosystem, has introduced the Digital Medical Products Act (DMPA), one of the world’s first comprehensive statutory frameworks for learning medical AI. Despite philosophical differences, these regulatory trajectories converge on a shared insight: learning AI systems cannot be governed by static rules or episodic evaluation. This viewpoint proposes Co-Lifecycle Governance as a conceptual framework to synchronize regulatory oversight with adaptive intelligence. Rather than treating oversight as a discrete event, Co-Lifecycle Governance frames regulation as a continuous, synchronized process grounded in 4 pillars: continuous validation, agile change management, proactive performance surveillance, and distributed accountability. Each pillar functions as a structural antidote to the responsibility frictions that arise when AI systems evolve faster than expectations surrounding them. Together, these pillars provide a governance grammar capable of supporting safe, iterative model improvement while maintaining system-level trust. Drawing from the strengths of US predictability, European Union accountability, and Korean scalability, this paper outlines a hybrid convergence pathway that synthesizes predictability, accountability, and operational feasibility. Learning AI will not wait for governance to catch up; oversight must evolve in lockstep with adaptive intelligence. Co-Lifecycle Governance offers a foundation for regulatory systems that not only regulate learning AI but also learn with it—at the speed at which adaptive intelligence actually changes.

Why People Conceal Mental Health Problems: Qualitative Analysis of TikTok Posts

2026-05-19T15:15:17-04:00

Background: Concealment of psychiatric symptoms is a barrier to effective mental health treatment, particularly among patients with suicidal thoughts and behaviors. Prior research on concealment has relied on retrospective self-report or laboratory-based interviews, which may not capture real-world decision-making about disclosure. Social media platforms such as TikTok provide a context in which individuals publicly narrate their experiences about concealing psychiatric symptoms, offering insight into motivations for concealment uninfluenced by experimenter demand characteristics. Objective: To understand patient decision-making about when to conceal and when to disclose psychiatric symptoms, this study examined social media content about patient experiences of concealing mental health symptoms. TikTok was chosen because it is the fastest-growing social media platform, and social media platforms provide an open-ended format for people to express their thoughts and feelings on various topics. Methods: Using a newly created TikTok account to minimize algorithmic bias, we identified and downloaded the 25 most-viewed English-language videos from 4 search terms about concealment in clinical contexts (“lying to therapist,” “lying to my therapist,” “lying to doctor about mental health,” and “lying to doctors about mental health”). After exclusions, 98 videos were included in the analysis. Videos were analyzed using reflexive thematic analysis. Four coders collaboratively developed a codebook through iterative review, triangulation, and consensus discussions. Engagement metrics (views, likes, comments, shares, saves) were recorded and summarized. Results: The 98 videos had 73,252,531 views, 14,356,874 likes, 74,954 comments, 770,027 shares, and 1,204,006 saves. Four themes were constructed among the 90 videos that explicitly discussed motivations for concealment: (1) disclosure perceived as punitive (31/90, 34.4% of videos), including desire to avoid hospitalization (17/90, 18.8%); (2) managing others’ feelings and impressions (28/90, 31.1%), including fear of upsetting therapists (5/90, 5.5%) and maintaining a façade of wellness (7/90, 7.7%); (3) negative emotions or inability to identify feelings (21/90, 23.3%), including fear of vulnerability (6/90, 6.6%); and (4) negative opinions of psychiatric treatment (17/90, 18.8%), including concerns about confidentiality (3/90, 3.3%). An exploratory theme captured ambivalence and guilt surrounding nondisclosure. Conclusions: Results provide insight into patient motivations for concealing their suicidal thoughts and behaviors and offer potential avenues for improving rates of disclosure, which is critical to reducing death by suicide. TikTok creators frequently described concealment as a strategy to avoid perceived punitive consequences, manage interpersonal dynamics, or cope with emotional distress. Findings suggest that current risk management practices and stigma surrounding psychiatric care may unintentionally reinforce concealment behaviors. These insights may inform interventions aimed at improving the therapeutic alliance, enhancing transparency around hospitalization criteria, and reducing barriers to honest reporting of suicide risk.

Efficacy of Digital Speech Therapy for Poststroke Dysarthria: Randomized Noninferiority Trial

2026-05-18T13:45:17-04:00

Background: Poststroke dysarthria, a common speech impairment, affects up to half of all stroke survivors, often reducing their ability to communicate, and adversely affecting their quality of life. Although conventional speech therapy for poststroke dysarthria is effective, access is often limited by time and geographical constraints. Here, digital speech therapy may serve as a remotely deliverable alternative for selected patients. However, few trials have assessed its efficacy, safety, and usability. Objective: This study aimed to evaluate whether a smartphone-based speech therapy app is noninferior to conventional workbook-based therapy in improving speech intelligibility among individuals with poststroke dysarthria. Methods: This single-blind, randomized controlled, noninferiority trial was performed at 3 hospitals in South Korea. Adults (≥19 y) with poststroke dysarthria who were cognitively intact, without aphasia, and able to use a smartphone were eligible. Participants were enrolled between July 20, 2023, and April 15, 2024. Participants were randomly assigned (1:1), stratified by stroke phase, using a block randomization method, to receive either a smartphone-based digital therapy app or a conventional workbook-based therapy for 4 weeks. The primary outcome was speech intelligibility (0‐100 perceptual rating) after the intervention. Primary analysis was intention-to-treat using analysis of covariance. A noninferiority margin of 19 points was pre-defined. Results: A total of 73 participants were enrolled (median age 62.00 years). Among them, 38 were assigned to the digital speech therapy group and 35 to the control group. Intelligibility scores improved from 80.48 (SD 18.92) to 92.08 (SD 12.38) in the intervention group, and from 80.94 (SD 16.74) to 88.11 (SD 18.06) in the control group. The adjusted between-group difference was 4.49 (95% CI 0.61-8.37), and the lower bound of the 95% CI was above the prespecified noninferiority margin (–19), which supported noninferiority. No significant between-group differences were observed in the secondary outcomes related to speech function or psychological status. The system usability score was 89.6, and adherence in the digital speech therapy group was 64.6% based on app logs, with no treatment-related adverse events. Conclusions: Digital speech therapy was noninferior to conventional workbook-based therapy in improving speech intelligibility and was feasible across acute to early subacute and chronic stroke phases in cognitively intact stroke survivors with predominantly mild-to-moderate dysarthria. However, feasibility and efficacy in older stroke survivors with cognitive deficits or co-occurring aphasia, or in those unable to use smartphones, remain to be established. Trial Registration: : ClinicalTrials.gov NCT05865106; https://clinicaltrials.gov/study/NCT05865106 International Registered Report Identifier (IRRID): RR2-10.3389/fneur.2024.1305297

Digital Patient Decision Aid for Antiobesity Medications: Mixed Methods Study of Human-Centered Design and Usability Evaluation

2026-05-15T18:00:05-04:00

Background: The global burden of obesity continues to rise, highlighting the need for patient-centered approaches to weight management. Shared decision-making is particularly important in the selection of antiobesity medications (AOMs), as treatment options differ in mechanism, effectiveness, side effects, routes of administration, and cost. Despite this preference-sensitive context, only a few patient decision aids (PDAs) have been culturally and clinically adapted for use in Asian populations. Objective: This study aims to design, develop, and evaluate a digital PDA, OptiWeight, to support shared decision-making for AOM selection, incorporating perspectives from health care professionals and patients. Methods: This mixed methods, multicenter study, conducted between August 2022 and November 2025, applied a 4-stage human-centered design process. An evidence-informed prototype was developed based on clinical guidelines, followed by 2 rounds of usability testing using think-aloud protocols to assess navigation structures, perceived usability (System Usability Scale [SUS]), and cognitive workload (NASA Task Load Index [NASA-TLX]). Semistructured interviews with health care professionals specializing in weight management, guided by the Consolidated Framework for Implementation Research, informed clinical implementation and workflow integration. Finally, patients with overweight or obesity evaluated usability, cognitive workload, and overall user experience in outpatient settings. Qualitative data were analyzed using content analysis, and 1-way analysis of variance examined changes in usability and workload across stages. Results: A total of 174 individuals were included across all study stages (usability testing among adults: n=78; health care professional interviews: n=18; and clinical evaluation among patients: n=78). Iterative usability testing comparing system- and user-controlled navigation structures revealed complementary strengths and limitations, leading to the adoption of a hybrid navigation structure supporting both sequential guidance and flexible comparison. Additional design requirements included the use of icon arrays to enhance risk comprehension and localization features such as treatment cost displays and clarification of socially impactful side effects. Perceived usability increased from initial testing to clinical evaluation (SUS: 60.53-73.65, P<.001), meeting good usability thresholds, while cognitive workload decreased (NASA-TLX: 40.35-16.69, P<.001). Conclusions: Through a systematic human-centered design process integrating health care professional and patient perspectives, OptiWeight addresses the lack of culturally adapted PDAs for AOM decision-making in Mandarin-speaking populations while capturing user needs—particularly regarding navigation flexibility and risk visualization. The final tool demonstrated good usability and feasibility, and workflow considerations suggest potential for integration into routine weight-management care. Further research is needed to evaluate its impact on decision quality and real-world implementation outcomes.

Assessing the Use of Wearable Mobile-Monitoring Devices Among Individuals With Serious Mental Illness: Qualitative Acceptability and Feasibility Study

2026-05-15T17:30:17-04:00

Background: Serious mental illness (SMI) is difficult to treat for various reasons, such as rapid changes in symptoms, comorbid health conditions, long gaps between provider visits, and additional societal barriers experienced by this population. Wearable mobile-sensing devices can be used to passively collect valuable patient-generated health data, such as daily step count, heart rate variability, sleep information, and other health-related behaviors, which could inform and improve treatment for individuals with SMI. Wearable health devices have become more economically accessible, providing promise for the possibility of their implementation in health care. However, more information regarding how individuals with SMI perceive and interact with these devices is needed. Objective: This study aimed to assess the acceptability and feasibility of using wearable mobile-sensing devices to improve treatment outcomes for Veterans with SMI. In addition, we were also interested in learning if privacy concerns would influence acceptability of devices, specifically surrounding location tracking and health information sharing, as well as assessing other barriers to device use. Methods: Qualitative interviews were conducted with participants who had been using a wearable health and fitness tracker for at least 2 weeks to explore their thoughts and perceptions of these devices. A total of 15 Veterans diagnosed with a SMI participated in interviews. Both thematic analysis and rapid qualitative analysis approaches were used to uncover findings in key domains and emergent themes. Results: Wearable fitness trackers allowed participants to conveniently monitor various aspects of their physical and mental health, provided a greater understanding of their overall well-being, and motivated them to reach personal health goals. Individuals were open to sharing their personal health information collected from the devices with providers to improve their health care treatment and expressed no privacy concerns surrounding data tracking or the device’s global positioning system that monitors physical location. Participants experienced some technological challenges with using the fitness trackers, as well as the device’s accompanying cell phone app. Furthermore, participants expressed difficulties in understanding and interpreting the health data that was collected from the health and fitness trackers. Greater ongoing technological support, in addition to physical device adjustments to enhance comfort and usability, were suggested ways of improving overall user experience. Conclusions: Participants with SMI in this sample were accepting of wearable mobile-monitoring devices and believe it is feasible to incorporate these fitness trackers into their daily lives. Furthermore, participants in this sample expressed no privacy concerns regarding location tracking or the sharing of health information collected from these devices with providers. Patient-generated health data collected from these devices may offer valuable information that could be used to inform health care treatment for this population.

Concerns of Using Large Language Models in Health Care Research and Practice: Umbrella Review

2026-05-15T16:15:15-04:00

Background: Large language models (LLMs), such as ChatGPT (OpenAI), are rapidly evolving, and their applications in health care are increasing. There is a growing demand for automation of routine tasks and a drive to use LLMs or similar to support research. Objective: This umbrella review examines concerns of health care professionals and researchers related to the use of LLMs in health care research and practice. We aimed to identify common issues raised and the implications for patient care, policy, and practice. Methods: A protocol was registered on PROSPERO (CRD420250640997). Searches were conducted in 7 databases (Ovid MEDLINE, Ovid Embase, Scopus, Web of Science, JBI Database of Systematic Reviews and Implementation Reports, Cochrane Database of Systematic Reviews, and Epistemonikos) in February 2025 and updated in February 2026. Screening was conducted in 2 stages, with independent screening by 2 reviewers. Studies published in the English language after January 2017 with at least one outcome expressing concerns of LLM or generative artificial intelligence use in health care research were included. The included studies were quality appraised for risk of bias and certainty of the evidence using AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation), respectively. Data was extracted using a piloted form and narratively synthesized following SWiM guidelines and the PRIOR (Preferred Reporting Items for Overviews of Reviews) checklist. Results: The search retrieved 448 systematic reviews, of which 42 met the inclusion criteria. Further, 12 distinct populations were identified, including researchers and clinicians in various medical specialties. The included reviews were assessed to be of very poor quality, and the level of overlap between primary studies could not be determined. Additionally, 15 reviews focused on ChatGPT, a further 15 on two or more LLMs, and 12 on generic artificial intelligence. Thus, 3 main themes emerged from the narrative synthesis. In order of most to least frequently discussed: (1) technical capability; (2) ethical, legal, and societal; and (3) costs. Conclusions: To our knowledge, this is the first umbrella review to address the concerns of LLMs in health care research and practice. Thematic analyses provided insight into the complexity of different perspectives, and by using a whole population approach, it demonstrates common narratives. However, the poor quality of the included studies and potential overlap of results are substantial limitations. Data quality is at the heart of these concerns, and combative action must ensure health care professionals and researchers have the resources required to overcome these apprehensions. Ethical, legal, and societal implications of artificial intelligence use were also commonly raised. As technology accelerates and demands on health care increase, we must adapt and embrace change with equity, diversity, inclusion, and safety at the core. Trial Registration: PROSPERO CRD420250640997; https://www.crd.york.ac.uk/PROSPERO/view/CRD420250640997