BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items.
OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app.
METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants\' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses.
RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants\' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory.
CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

OBJECTIVE: Artificial intelligence (AI) in positron emission tomography/computed tomography (PET/CT) can be used to improve image quality when it is useful to reduce the injected activity or the acquisition time. Particular attention must be paid to ensure that users adopt this technological innovation when outcomes can be improved by its use. The aim of this study was to identify the aspects that need to be analysed and discussed to implement an AI denoising PET/CT algorithm in clinical practice, based on the representations of Nuclear Medicine Technologists (NMT) from Western-Switzerland, highlighting the barriers and facilitators associated.
METHODS: Two focus groups were organised in June and September 2023, involving ten voluntary participants recruited from all types of medical imaging departments, forming a diverse sample of NMT. The interview guide followed the first stage of the revised model of Ottawa of Research Use. A content analysis was performed following the three-stage approach described by Wanlin. Ethics cleared the study.
RESULTS: Clinical practice, workload, knowledge and resources were de 4 themes identified as necessary to be thought before implementing an AI denoising PET/CT algorithm by ten NMT participants (aged 31-60), not familiar with this AI tool. The main barriers to implement this algorithm included workflow challenges, resistance from professionals and lack of education; while the main facilitators were explanations and the availability of support to ask questions such as a \"local champion\".
CONCLUSIONS: To implement a denoising algorithm in PET/CT, several aspects of clinical practice need to be thought to reduce the barriers to its implementation such as the procedures, the workload and the available resources. Participants emphasised also the importance of clear explanations, education, and support for successful implementation.
CONCLUSIONS: To facilitate the implementation of AI tools in clinical practice, it is important to identify the barriers and propose strategies that can mitigate it.

BACKGROUND: Physicians and patient-facing caregivers have increasingly used mobile health (mHealth) technologies in the past several years, accelerating during the COVID-19 pandemic. However, barriers and feedback surrounding adoption remain relatively understudied and varied across health systems, particularly in rural areas.
OBJECTIVE: This study aims to identify provider adoption, attitudes, and barriers toward mHealth in a large, multisite, rural US health care system. We investigated (1) mHealth apps that providers use for their own benefit and (2) mHealth apps that a provider uses in conjunction with a patient.
METHODS: We surveyed all patient-seeing providers within the Marshfield Clinic Health System with a brief, 16-item, web-based survey assessing attitudes toward mHealth, adoption of these technologies, and perceived barriers faced by providers, their peers, and the institution. Survey results were summarized via descriptive statistics, with log-binomial regression and accompanying pairwise analyses, using Kruskal-Wallis and Jonckheere-Terpstra tests for significance, respectively. Respondents were grouped by reported clinical role and specialty.
RESULTS: We received a 38% (n/N=916/2410) response rate, with 60.7% (n=556) of those sufficiently complete for analyses. Roughly 54.1% (n=301) of respondents reported mHealth use, primarily around decision-making and supplemental information, with use differing based on provider role and years of experience. Self-reported barriers to using mHealth included a lack of knowledge and time to study mHealth technologies. Providers also reported concerns about patients\' internet access and the complexity of mHealth apps to adequately use mHealth technologies. Providers believed the health system\'s barriers were largely privacy, confidentiality, and legal review concerns.
CONCLUSIONS: These findings echo similar studies in other health systems, surrounding providers\' lack of time and concerns over privacy and confidentiality of patient data. Providers emphasized concerns over the complexity of these technologies for their patients and concerns over patients\' internet access to fully use mHealth in their delivery of care.

BACKGROUND: The use of single combination inhaled corticosteroid (ICS) and long-acting bronchodilator for maintenance and relief (MART) significantly reduces asthma exacerbations and has been incorporated into asthma guidelines since December 2020, but there is limited data regarding the implementation of this approach to asthma management.
OBJECTIVE: Determine how often MART was prescribed to patients with moderate to severe asthma being seen at subspecialty pulmonary and allergy practices at an academic health care system, and the patient and clinician characteristics associated with the use of MART.
METHODS: We conducted a retrospective cross-sectional study of the EMR of an academic health care system in the Northeastern US between January 2021 and October 2023. Patient demographic and clinician data was collected, and MART recommendation was confirmed by chart review. We assessed the relationships between patient demographics, clinician characteristics, and MART recommendation.
RESULTS: Of 2,016 patients reviewed, 293 (14.5%) were recommended MART, with 255 (87%) concurrently prescribed short acting bronchodilators. Patients on ICS/formoterol at baseline were significantly more likely to be recommended MART, while older patients and those on Medicare were significantly less likely to be recommended MART. Twenty-two (44%) of 50 clinicians did not recommend MART ever and only three clinicians recommended MART to 30-60% of their patients. Clinicians who were part of the asthma group and those with less than 16 years in practice were significantly more likely to recommend MART.
CONCLUSIONS: Among academic subspecialty clinicians, there has been limited implementation of MART, with a small number of clinicians adopting MART routinely and over 40% of clinicians not recommending it.

Understanding the impacts of bias, and how to mitigate these impacts, on clinical care is critically important for all healthcare team members. However, the concerns and needs in our current system are likely even more fundamental, as we are continuing to hear about the experiences of patients who are struggling to seek care that contains even the most basic tenants of respect and decency. Creating inclusive and diverse environments requires constant proactive evaluation, commitment, and energy. This piece shares the experiences of a Black birth mom and a White adoptive dad (who is also an Ob/Gyn and anti-racism researcher) and the experiences surrounding the birth of their daughter.

BACKGROUND: The escalating prevalence of diabetes, with its multifaceted complications, poses a pressing challenge for healthcare systems globally. In response, the advent of continuous glucose monitoring (CGM) systems, offering technological solutions for daily diabetes management, presents significant opportunities. However, the widespread adoption faces several barriers, linked both to the technological configuration of the devices and to the psychological dimension of patients. Therefore, this study aims to apply and test a theoretical model that investigates the antecedents of the intention to use Continuous Glucose Monitoring systems.
METHODS: The research model was built to unveil the impacts of psychological factors, functional components and rational constructs derived from the Technology Acceptance Model (TAM) on CGM systems sustained adoption. To ensure the comparability of results, we have collected data from people who had used Dexcom ONE Dexcom (San Diego, CA) for the first time for at least one month. Employing Structural Equation Modelling (SEM) techniques, the hypothesized relationships among constructs were assessed.
RESULTS: The analyses confirmed the positive correlation of rational factors to the Intention to Use. Subjective Norm, intended as the physicians\' influence, is positively correlated with the Perceived Usefulness. Trend Arrows, albeit being negatively correlated with Perceived Usefulness, have a positive correlation on Perceived Ease Of Use, reinforcing its mediating effect towards Perceived Usefulness. Among psychological factors, Trust in the CGM technology positively correlates with Intention to Use. Health Literacy is negatively correlated to the Intention to Use.
CONCLUSIONS: These findings contribute to theoretical and managerial understanding, providing recommendations to enhance the adoption of CGM systems like Dexcom ONE.

Introduction: Diffusion of Innovation Theory explains how ideas or products gain momentum and diffuse (or spread) through specific populations or social systems over time. The theory analyzes primary influencers of the spread of new ideas, including the innovation itself, communication channels, time, and social systems. Methods: The current study reviewed published medical literature to identify studies and applications of artificial intelligence (AI) in endourology and used E.M. Rogers\' Diffusion of Innovation Theory to analyze the primary influencers of the adoption of AI in endourological care. The insights gained were triaged and prioritized into AI application-related action items or \"tips\" for facilitating the appropriate diffusion of the most valuable endourological innovations. Results: Published medical literature indicates that AI is still a research-based tool in endourology and is not widely used in clinical practice. The published studies have presented AI models and algorithms to assist with stone disease detection (n = 17), the prediction of management outcomes (n = 18), the optimization of operative procedures (n = 9), and the elucidation of stone disease chemistry and composition (n = 24). Five tips for facilitating appropriate adoption of endourological AI are: (1) Develop/prioritize training programs to establish the foundation for effective use; (2) create appropriate data infrastructure for implementation, including its maintenance and evolution over time; (3) deliver AI transparency to gain the trust of endourology stakeholders; (4) adopt innovations in the context of continuous quality improvement Plan-Do-Study-Act cycles as these approaches have proven track records for improving care quality; and (5) be realistic about what AI can/cannot currently do and document to establish the basis for shared understanding. Conclusion: Diffusion of Innovation Theory provides a framework for analyzing the influencers of the adoption of AI in endourological care. The five tips identified through this research may be used to facilitate appropriate diffusion of the most valuable endourological innovations.

BACKGROUND: The use of artificial intelligence (AI) for pain assessment has the potential to address historical challenges in infant pain assessment. There is a dearth of information on the perceived benefits and barriers to the implementation of AI for neonatal pain monitoring in the neonatal intensive care unit (NICU) from the perspective of health care professionals (HCPs) and parents. This qualitative analysis provides novel data obtained from 2 large tertiary care hospitals in Canada and the United Kingdom.
OBJECTIVE: The aim of the study is to explore the perspectives of HCPs and parents regarding the use of AI for pain assessment in the NICU.
METHODS: In total, 20 HCPs and 20 parents of preterm infants were recruited and consented to participate from February 2020 to October 2022 in interviews asking about AI use for pain assessment in the NICU, potential benefits of the technology, and potential barriers to use.
RESULTS: The 40 participants included 20 HCPs (17 women and 3 men) with an average of 19.4 (SD 10.69) years of experience in the NICU and 20 parents (mean age 34.4, SD 5.42 years) of preterm infants who were on average 43 (SD 30.34) days old. Six themes from the perspective of HCPs were identified: regular use of technology in the NICU, concerns with regard to AI integration, the potential to improve patient care, requirements for implementation, AI as a tool for pain assessment, and ethical considerations. Seven parent themes included the potential for improved care, increased parental distress, support for parents regarding AI, the impact on parent engagement, the importance of human care, requirements for integration, and the desire for choice in its use. A consistent theme was the importance of AI as a tool to inform clinical decision-making and not replace it.
CONCLUSIONS: HCPs and parents expressed generally positive sentiments about the potential use of AI for pain assessment in the NICU, with HCPs highlighting important ethical considerations. This study identifies critical methodological and ethical perspectives from key stakeholders that should be noted by any team considering the creation and implementation of AI for pain monitoring in the NICU.

BACKGROUND: The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively.
OBJECTIVE: This study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy.
METHODS: We conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi).
RESULTS: In total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings.
CONCLUSIONS: Gastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.

BACKGROUND: The world has witnessed increased adoption of large language models (LLMs) in the last year. Although the products developed using LLMs have the potential to solve accessibility and efficiency problems in health care, there is a lack of available guidelines for developing LLMs for health care, especially for medical education.
OBJECTIVE: The aim of this study was to identify and prioritize the enablers for developing successful LLMs for medical education. We further evaluated the relationships among these identified enablers.
METHODS: A narrative review of the extant literature was first performed to identify the key enablers for LLM development. We additionally gathered the opinions of LLM users to determine the relative importance of these enablers using an analytical hierarchy process (AHP), which is a multicriteria decision-making method. Further, total interpretive structural modeling (TISM) was used to analyze the perspectives of product developers and ascertain the relationships and hierarchy among these enablers. Finally, the cross-impact matrix-based multiplication applied to a classification (MICMAC) approach was used to determine the relative driving and dependence powers of these enablers. A nonprobabilistic purposive sampling approach was used for recruitment of focus groups.
RESULTS: The AHP demonstrated that the most important enabler for LLMs was credibility, with a priority weight of 0.37, followed by accountability (0.27642) and fairness (0.10572). In contrast, usability, with a priority weight of 0.04, showed negligible importance. The results of TISM concurred with the findings of the AHP. The only striking difference between expert perspectives and user preference evaluation was that the product developers indicated that cost has the least importance as a potential enabler. The MICMAC analysis suggested that cost has a strong influence on other enablers. The inputs of the focus group were found to be reliable, with a consistency ratio less than 0.1 (0.084).
CONCLUSIONS: This study is the first to identify, prioritize, and analyze the relationships of enablers of effective LLMs for medical education. Based on the results of this study, we developed a comprehendible prescriptive framework, named CUC-FATE (Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability), for evaluating the enablers of LLMs in medical education. The study findings are useful for health care professionals, health technology experts, medical technology regulators, and policy makers.