Perspectives on virtual interviews and emerging technologies integration in family medicine residency programs: a cross-sectional survey study.

BACKGROUND: During the coronavirus disease of 2019 (COVID-19) pandemic, in-person interviews for the recruitment of family medicine residents shifted to online (virtual) interviews. The purpose of this study was twofold: (1) to gather the ideas about virtual interviews of family medicine applicants (interviewees), and faculty and staff who interviewed these applicants (interviewers), and (2) to describe interviewers' and interviewees' opinions of use of emerging technologies such as artificial intelligence (AI) and virtual reality (VR) in the recruitment process as well as during clinical practice. METHODS: This was a cross-sectional survey study. Participants were both interviewers and candidates who applied to the McGill University Family Medicine Residency Program for the 2020-2021 and 2021-2022 cycles. RESULTS: The study population was constituted by N = 132 applicants and N = 60 interviewers. The response rate was 91.7% (55/60) for interviewers and 43.2% (57/132) for interviewees. Both interviewers (43.7%) and interviewees (68.5%) were satisfied with connecting through virtual interviews. Interviewers (43.75%) and interviewees (55.5%) would prefer for both options to be available. Both interviewers (50%) and interviewees (72%) were interested in emerging technologies. Almost all interviewees (95.8%) were interested in learning about AI and VR and its application in clinical practice with the majority (60.8%) agreeing that it should be taught within medical training. CONCLUSION: Although experience of virtual interviewing during the COVID-19 pandemic has been positive for both interviewees and interviewers, the findings of this study suggest that it will be unlikely that virtual interviews completely replace in-person interviews for selecting candidates for family medicine residency programs in the long term as participants value aspects of in-person interviews and would want a choice in format. Since incoming family medicine physicians seem to be eager to learn and utilize emerging technologies such as AI and VR, educators and institutions should consider family physicians' needs due to the changing technological landscape in family medicine education.

Explainable Machine Learning Model to Predict COVID-19 Severity Among Older Adults in the Province of Quebec.

Context: Patients over the age of 65 years are more likely to experience higher severity and mortality rates than other populations from COVID-19. Clinicians need assistance in supporting their decisions regarding the management of these patients. Artificial Intelligence (AI) can help with this regard. However, the lack of explainability-defined as "the ability to understand and evaluate the internal mechanism of the algorithm/computational process in human terms"-of AI is one of the major challenges to its application in health care. We know little about application of explainable AI (XAI) in health care. Objective: In this study, we aimed to evaluate the feasibility of the development of explainable machine learning models to predict COVID-19 severity among older adults. Design: Quantitative machine learning methods. Setting: Long-term care facilities within the province of Quebec. Participants: Patients 65 years and older presented to the hospitals who had a positive polymerase chain reaction test for COVID-19. Intervention: We used XAI-specific methods (e.g., EBM), machine learning methods (i.e., random forest, deep forest, and XGBoost), as well as explainable approaches such as LIME, SHAP, PIMP, and anchor with the mentioned machine learning methods. Outcome measures: Classification accuracy and area under the receiver operating characteristic curve (AUC). Results: The age distribution of the patients (n=986, 54.6% male) was 84.5□19.5 years. The best-performing models (and their performance) were as follows. Deep forest using XAI agnostic methods LIME (97.36% AUC, 91.65 ACC), Anchor (97.36% AUC, 91.65 ACC), and PIMP (96.93% AUC, 91.65 ACC). We found alignment with the identified reasoning of our models' predictions and clinical studies' findings-about the correlation of different variables such as diabetes and dementia, and the severity of COVID-19 in this population. Conclusions: The use of explainable machine learning models, to predict the severity of COVID-19 among older adults is feasible. We obtained a high-performance level as well as explainability in the prediction of COVID-19 severity in this population. Further studies are required to integrate these models into a decision support system to facilitate the management of diseases such as COVID-19 for (primary) health care providers and evaluate their usability among them.

Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of Artificial Intelligence for Better Health and Oral Health Care: A Workshop Summary.

Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University, built upon an intersectoral approach addressing equity, diversity and inclusion within the field of AI.

Determinants of technology adoption and continued use among cognitively impaired older adults: a qualitative study.

BACKGROUND: Technology offers opportunities to support older adults with mild cognitive impairments to remain independent and socially connected, but is often not used. Although determinants of technology use among older adults in general are well studied, much less is known about how these factors impact technology use behaviour in cognitively impaired older adults. This study aimed to bridge this gap in research by examining the factors underlying technology use in community-dwelling older adults with mild cognitive impairments. METHODS: We applied a generic qualitative design and used 16 semi-structured interviews to collect data from Belgian (Flemish) community-dwelling older adults diagnosed with Mild Cognitive Impairment or dementia and informal caregivers. To get data from different perspectives, a focus group with professional caregivers was added. We used thematic analysis with an inductive approach to identify and select themes from the data. RESULTS: We identified two themes: introduction of technology and determinants of technology adoption and continued use. Successful technology adoption in cognitively impaired older adults is need-driven and subject to individual, technological and contextual characteristics. Specific for older adults with cognitive impairments are the importance of disease awareness and cognitive ability for adoption and continued use, respectively. Although social support can be a valuable alternative to technology, it is an important facilitator of continued technology use in these older adults. Similarly, integration of technologies in daily routines can buffer discontinuation of technologies. CONCLUSIONS: Future research is encouraged to validate our findings in a postpandemic era and to further develop a novel theoretical framework for technology acceptance among older adults with cognitive impairments. Moreover, identification of crucial determinants as well as strategies to remove use barriers are also important future research tasks. Clinical practice should focus on improving disease awareness to facilitate technology adoption and policies should invest in training and support of professional caregivers and in reimbursement strategies to facilitate implementation of technology in practice.

Continuing professional education of Iranian healthcare professionals in shared decision-making: lessons learned.

BACKGROUND: In this study, we sought to assess healthcare professionals' acceptance of and satisfaction with a shared decision making (SDM) educational workshop, its impact on their intention to use SDM, and their perceived facilitators and barriers to the implementation of SDM in clinical settings in Iran. METHODS: We conducted an observational quantitative study that involved measurements before, during, and immediately after the educational intervention at stake. We invited healthcare professionals affiliated with Tabriz University of Medical Sciences, East Azerbaijan, Iran, to attend a half-day workshop on SDM in December 2016. Decisions about prenatal screening and knee replacement surgery was used as clinical vignettes. We provided a patient decision aid on prenatal screening that complied with the International Patient Decision Aids Standards and used illustrate videos. Participants completed a sociodemographic questionnaire and a questionnaire to assess their familiarity with SDM, a questionnaire based on theoretical domains framework to assess their intention to implement SDM, a questionnaire about their perceived facilitators and barriers of implementing SDM in their clinical practice, continuous professional development reaction questionnaire, and workshop evaluation. Quantitative data was analyzed descriptively and with multiple linear regression. RESULTS: Among the 60 healthcare professionals invited, 41 participated (68%). Twenty-three were female (57%), 18 were specialized in family and emergency medicine, or community and preventive medicine (43%), nine were surgeons (22%), and 14 (35%) were other types of specialists. Participants' mean age was 37.51 ± 8.64 years with 8.09 ± 7.8 years of clinical experience. Prior to the workshop, their familiarity with SDM was 3.10 ± 2.82 out of 9. After the workshop, their belief that practicing SDM would be beneficial and useful (beliefs about consequences) (beta = 0.67, 95% CI 0.27, 1.06) and beliefs about capability of using SDM (beta = 0.32, 95% CI -0.08, 0.72) had the strongest influence on their intention of practicing SDM. Participants perceived the main facilitator and barrier to perform SDM were training and high patient load, respectively. CONCLUSIONS: Participants thought the workshop was a good way to learn SDM and that they would be able to use what they had learned in their clinical practice. Future studies need to study the level of intention of participants in longer term and evaluate the impact of cultural differences on practicing SDM and its implementation in both western and non-western countries.

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal.

BACKGROUND: Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. OBJECTIVE: We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. METHODS: We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. RESULTS: We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). CONCLUSIONS: We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative.

AIM: To develop a consensus paper on the central points of an international invitational think-tank on nursing and artificial intelligence (AI). METHODS: We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3-day invitational think tank in autumn 2019. Activities included a pre-event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities. IMPLICATIONS FOR NURSING: Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice. CONCLUSION: There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems. IMPACT: We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.

Pregnant women's views on how to promote the use of a decision aid for Down syndrome prenatal screening: a theory-informed qualitative study.

BACKGROUND: For pregnant women and their partners, the decision to undergo Down syndrome prenatal screening is difficult. Patient decision aids (PtDA) can help them make an informed decision. We aimed to identify behaviour change techniques (BCTs) that would be useful in an intervention to promote the use of a PtDA for Down syndrome prenatal screening, and to identify which of these BCTs pregnant women found relevant and acceptable. METHODS: Using the Behaviour Change Wheel and the Theoretical Domains Framework, we conducted a qualitative descriptive study. First, a group of experts from diverse professions, disciplines and backgrounds (eg. medicine, engineering, implementation science, community and public health, shared decision making) identified relevant BCTs. Then we recruited pregnant women consulting for prenatal care in three clinical sites: a family medicine group, a birthing centre (midwives) and a hospital obstetrics department in Quebec City, Canada. To be eligible, participants had to be at least 18 years old, having recently given birth or at least 16 weeks pregnant with a low-risk pregnancy, and have already decided about prenatal screening. We conducted three focus groups and asked questions about the relevance and acceptability of the BCTs. We analysed verbatim transcripts and reduced the BCTs to those the women found most relevant and acceptable. RESULTS: Our group of experts identified 25 relevant BCTs relating to information, support, consequences, others' approval, learning, reward, environmental change and mode of delivery. Fifteen women participated in the study with a mean age of 27 years. Of these, 67% (n = 10) were pregnant for the first time, 20% (n = 3) had difficulty making the decision to take the test, and 73% had made the decision with their partner. Of the 25 BCTs identified using the Behaviour Change Wheel, the women found the following 10 to be most acceptable and relevant: goal setting (behaviour), goal setting (results), problem solving, action plan, social support (general), social support (practical), restructuring the physical environment, prompts/cues, credible sources and modelling or demonstration of the behaviour. CONCLUSIONS: An intervention to promote PtDA use among pregnant women for Down syndrome prenatal screening should incorporate the 10 BCTs identified.

Psychosocial Factors of Health Professionals' Intention to Use a Decision Aid for Down Syndrome Screening: Cross-Sectional Quantitative Study.

BACKGROUND: Decisions about prenatal screening for Down syndrome are difficult for women, as they entail risk, potential loss, and regret. Shared decision making increases women's knowledge of their choices and better aligns decisions with their values. Patient decision aids foster shared decision making but are rarely used in this context. OBJECTIVE: One of the most promising strategies for implementing shared decision making is distribution of decision aids by health professionals. We aimed to identify factors influencing their intention to use a DA during prenatal visit for decisions about Down syndrome screening. METHODS: We conducted a cross-sectional quantitative study. Using a Web panel, we conducted a theory-based survey of health professionals in Quebec province (Canada). Eligibility criteria were as follows: (1) family physicians, midwives, obstetrician-gynecologists, or trainees in these professions; (2) involved in prenatal care; and (3) working in Quebec province. Participants watched a video depicting a health professional using a decision aid during a prenatal consultation with a woman and her partner, and then answered a questionnaire based on an extended version of the theory of planned behavior, including some of the constructs of the theoretical domains framework. The questionnaire assessed 8 psychosocial constructs (attitude, anticipated regret, subjective norm, self-identity, moral norm, descriptive norm, self-efficacy, and perceived control), 7 related sets of behavioral beliefs (advantages, disadvantages, emotions, sources of encouragement or discouragement, incentives, facilitators, and barriers), and sociodemographic data. We performed descriptive, bivariate, and multiple linear regression analyses to identify factors influencing health professionals' intention to use a decision aid. RESULTS: Among 330 health professionals who completed the survey, 310 met the inclusion criteria: family physicians, 55.2% (171/310); obstetrician-gynecologists, 33.8% (105/310); and midwives, 11.0% (34/310). Of these, 80.9% were female (251/310). Mean age was 39.6 (SD 11.5) years. Less than half were aware of any decision aids at all. In decreasing order of importance, factors influencing their intention to use a decision aid for Down syndrome prenatal screening were as follows: self-identity (beta=.325, P<.001), attitude (beta=.297, P<.001), moral norm (beta=.288, P<.001), descriptive norm (beta=.166, P<.001), and anticipated regret (beta=.099, P=.003). Underlying behavioral beliefs significantly related to intention were that the use of a decision aid would promote decision making (beta=.117, 95% CI 0.043-0.190), would reassure health professionals (beta=.100, 95% CI 0.024-0.175), and might require more time than planned for the consultation (beta=-.077, 95% CI -0.124 to -0.031). CONCLUSIONS: We identified psychosocial factors that could influence health professionals' intention to use a decision aid about Down syndrome screening. Strategies should remind them of the following: (1) using a decision aid for this purpose should be a common practice, (2) it would be expected of someone in their societal role, (3) the experience of using it will be satisfying and reassuring, and (4) it is likely to be compatible with their moral values.

Implications of conscious AI in primary healthcare.

The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of AI identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients', healthcare workers' and policy-makers' attitudes towards consciousness of AI systems in primary healthcare settings.

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

BACKGROUND: The most common form of dementia, Alzheimer's Disease (AD), is challenging for both those affected as well as for their care providers, and caregivers. Socially assistive robots (SARs) offer promising supportive care to assist in the complex management associated with AD. OBJECTIVES: To conduct a scoping review of published articles that proposed, discussed, developed or tested SAR for interacting with AD patients. METHODS: We performed a scoping review informed by the methodological framework of Arksey and O'Malley and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. At the identification stage, an information specialist performed a comprehensive search of 8 electronic databases from the date of inception until January 2022 in eight bibliographic databases. The inclusion criteria were all populations who recive or provide care for AD, all interventions using SAR for AD and our outcomes of inteerst were any outcome related to AD patients or care providers or caregivers. All study types published in the English language were included. RESULTS: After deduplication, 1251 articles were screened. Titles and abstracts screening resulted to 252 articles. Full-text review retained 125 included articles, with 72 focusing on daily life support, 46 on cognitive therapy, and 7 on cognitive assessment. CONCLUSION: We conducted a comprehensive scoping review emphasizing on the interaction of SAR with AD patients, with a specific focus on daily life support, cognitive assessment, and cognitive therapy. We discussed our findings' pertinence relative to specific populations, interventions, and outcomes of human-SAR interaction on users and identified current knowledge gaps in SARs for AD patients.

Performance of generative pre-trained transformers (GPTs) in Certification Examination of the College of Family Physicians of Canada.

INTRODUCTION: The application of large language models such as generative pre-trained transformers (GPTs) has been promising in medical education, and its performance has been tested for different medical exams. This study aims to assess the performance of GPTs in responding to a set of sample questions of short-answer management problems (SAMPs) from the certification exam of the College of Family Physicians of Canada (CFPC). METHOD: Between August 8th and 25th, 2023, we used GPT-3.5 and GPT-4 in five rounds to answer a sample of 77 SAMPs questions from the CFPC website. Two independent certified family physician reviewers scored AI-generated responses twice: first, according to the CFPC answer key (ie, CFPC score), and second, based on their knowledge and other references (ie, Reviews' score). An ordinal logistic generalised estimating equations (GEE) model was applied to analyse repeated measures across the five rounds. RESULT: According to the CFPC answer key, 607 (73.6%) lines of answers by GPT-3.5 and 691 (81%) by GPT-4 were deemed accurate. Reviewer's scoring suggested that about 84% of the lines of answers provided by GPT-3.5 and 93% of GPT-4 were correct. The GEE analysis confirmed that over five rounds, the likelihood of achieving a higher CFPC Score Percentage for GPT-4 was 2.31 times more than GPT-3.5 (OR: 2.31; 95% CI: 1.53 to 3.47; p<0.001). Similarly, the Reviewers' Score percentage for responses provided by GPT-4 over 5 rounds were 2.23 times more likely to exceed those of GPT-3.5 (OR: 2.23; 95% CI: 1.22 to 4.06; p=0.009). Running the GPTs after a one week interval, regeneration of the prompt or using or not using the prompt did not significantly change the CFPC score percentage. CONCLUSION: In our study, we used GPT-3.5 and GPT-4 to answer complex, open-ended sample questions of the CFPC exam and showed that more than 70% of the answers were accurate, and GPT-4 outperformed GPT-3.5 in responding to the questions. Large language models such as GPTs seem promising for assisting candidates of the CFPC exam by providing potential answers. However, their use for family medicine education and exam preparation needs further studies.

Existing Digital Health Technology Index Summary Report for Older Adults Living with Neurocognitive Disorders (Mild and Major) and Their Informal Caregivers: An Environmental Scan.

Digital health has added numerous promising solutions to enhance the health and wellness of people with neurocognitive disorders (NCDs) and their informal caregivers. (1) Background: It is important to obtain a comprehensive view of currently available technologies, their outcomes, and conditions of success to inform recommendations regarding digital health solutions for people with NCDs and their caregivers. This environmental scan was performed to identify the features of existing digital health solutions relevant to the targeted population. This work reviews currently available digital health solutions and their related characteristics to develop a decision support tool for older adults living with mild or major neurocognitive disorders and their informal caregivers. This knowledge will aid the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate digital health solutions according to their needs and preferences based on trustable information. (2) Methods: We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches in the grey literature covering the regions of Canada and Europe. Technological tools were scanned based on a preformatted extraction grid. We assessed their relevance based on selected attributes and summarized the findings. (3) Results: We identified 100 available digital health solutions. The majority (56%) were not specific to NCDs. Only 28% provided scientific evidence of their effectiveness. Remote patient care, movement tracking, and cognitive exercises were the most common purposes of digital health solutions. Most solutions were presented as decision aid tools, pill dispensers, apps, web, or a combination of these platforms. (4) Conclusions: This environmental scan allowed for identifying current digital health solutions for older adults with mild or major neurocognitive disorders and their informal caregivers. Findings from the environmental scan highlight the need for additional approaches to strengthen digital health interventions for the well-being of older adults with mild and major NCDs and their informal and formal healthcare providers.

Primary care physicians' perceptions of artificial intelligence systems in the care of adolescents' mental health.

BACKGROUND: Given that mental health problems in adolescence may have lifelong impacts, the role of primary care physicians (PCPs) in identifying and managing these issues is important. Artificial Intelligence (AI) may offer solutions to the current challenges involved in mental health care. We therefore explored PCPs' challenges in addressing adolescents' mental health, along with their attitudes towards using AI to assist them in their tasks. METHODS: We used purposeful sampling to recruit PCPs for a virtual Focus Group (FG). The virtual FG lasted 75 minutes and was moderated by two facilitators. A life transcription was produced by an online meeting software. Transcribed data was cleaned, followed by a priori and inductive coding and thematic analysis. RESULTS: We reached out to 35 potential participants via email. Seven agreed to participate, and ultimately four took part in the FG. PCPs perceived that AI systems have the potential to be cost-effective, credible, and useful in collecting large amounts of patients' data, and relatively credible. They envisioned AI assisting with tasks such as diagnoses and establishing treatment plans. However, they feared that reliance on AI might result in a loss of clinical competency. PCPs wanted AI systems to be user-friendly, and they were willing to assist in achieving this goal if it was within their scope of practice and they were compensated for their contribution. They stressed a need for regulatory bodies to deal with medicolegal and ethical aspects of AI and clear guidelines to reduce or eliminate the potential of patient harm. CONCLUSION: This study provides the groundwork for assessing PCPs' perceptions of AI systems' features and characteristics, potential applications, possible negative aspects, and requirements for using them. A future study of adolescents' perspectives on integrating AI into mental healthcare might contribute a fuller understanding of the potential of AI for this population.

Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review.

BACKGROUND: The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process. OBJECTIVE: The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians. METHODS: We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. RESULTS: Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs. CONCLUSIONS: This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.11124/JBIES-22-00374.

Family medicine residents' perspectives on shared decision-making: A mixed methods study.

OBJECTIVES: To 1) examine the willingness of residents to undertake shared decision-making and 2) explore whether the willingness to engage in shared decision-making is influenced by the perceived stakes of a clinical situation. METHODS: Sequential mixed methods design. Phase One: Family Medicine residents completed IncorpoRATE, a seven-item measure of clinician willingness to engage in shared decision making. Mean IncorpoRATE scores were calculated. Phase Two: We interviewed residents from phase one to explore their perceptions of high versus low stakes situations. Transcripts were analyzed using qualitative content analysis. RESULTS: IncorpoRATE scores indicated a greater willingness to engage in shared decision-making when the stakes of the decision were perceived as low (7.59 [2.0]) compared to high (4.38 [2.5]). Interviews revealed that residents held variable views of the stakes of similar clinical decisions. CONCLUSION: Residents are more willing to engage in shared decision-making when the stakes of the situation are perceived to be low. However, the interpretation of the stakes of clinical situations varies. PRACTICAL IMPLICATIONS: Further research is needed to explore how shared decision making is understood by residents in Family Medicine and when they view the process of shared decision-making to be most appropriate.

Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review.

BACKGROUND: Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. Despite the recognition of the importance of this problem, there is a lack of research that specifically examines the strategies used to mitigate age-related bias in ML models and the effectiveness of these strategies. OBJECTIVE: To address this gap, we conducted a scoping review of mitigation strategies to reduce age-related bias in ML. METHODS: We followed a scoping review methodology framework developed by Arksey and O'Malley. The search was developed in conjunction with an information specialist and conducted in 6 electronic databases (IEEE Xplore, Scopus, Web of Science, CINAHL, EMBASE, and the ACM digital library), as well as 2 additional gray literature databases (OpenGrey and Grey Literature Report). RESULTS: We identified 8 publications that attempted to mitigate age-related bias in ML approaches. Age-related bias was introduced primarily due to a lack of representation of older adults in the data. Efforts to mitigate bias were categorized into one of three approaches: (1) creating a more balanced data set, (2) augmenting and supplementing their data, and (3) modifying the algorithm directly to achieve a more balanced result. CONCLUSIONS: Identifying and mitigating related biases in ML models is critical to fostering fairness, equity, inclusion, and social benefits. Our analysis underscores the ongoing need for rigorous research and the development of effective mitigation approaches to address digital ageism, ensuring that ML systems are used in a way that upholds the interests of all individuals. TRIAL REGISTRATION: Open Science Framework AMG5P; https://osf.io/amg5p.

Integrating equity, diversity and inclusion throughout the lifecycle of AI within healthcare: a scoping review protocol.

INTRODUCTION: Artificial intelligence (AI) has the potential to improve efficiency and quality of care in healthcare settings. The lack of consideration for equity, diversity and inclusion (EDI) in the lifecycle of AI within healthcare settings may intensify social and health inequities, potentially causing harm to under-represented populations. This article describes the protocol for a scoping review of the literature relating to integration of EDI in the AI interventions within healthcare setting. The objective of the review is to evaluate what has been done on integrating EDI concepts, principles and practices in the lifecycles of AI interventions within healthcare settings. It also aims to explore which EDI concepts, principles and practices have been integrated into the design, development and implementation of AI in healthcare settings. METHOD AND ANALYSIS: The scoping review will be guided by the six-step methodological framework developed by Arksey and O'Malley supplemented by Levac et al, and Joanna Briggs Institute methodological framework for scoping reviews. Relevant literature will be identified by searching seven electronic databases in engineering/computer science and healthcare, and searching the reference lists and citations of studies that meet the inclusion criteria. Studies on AI in any healthcare and geographical settings, that have considered aspects of EDI, published in English and French between 2005 and present will be considered. Two reviewers will independently screen titles, abstracts and full-text articles according to inclusion criteria. We will conduct a thematic analysis and use a narrative description to describe the work. Any disagreements will be resolved through discussion with the third reviewer. Extracted data will be summarised and analysed to address aims of the scoping review. Reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews. The study began in April 2022 and is expected to end in September 2023. The database initial searches resulted in 5,745 records when piloted in April 2022. ETHICS AND DISSEMINATION: Ethical approval is not required. The study will map the available literature on EDI concepts, principles and practices in AI interventions within healthcare settings, highlight the significance of this context, and offer insights into the best practices for incorporating EDI into AI-based solutions in healthcare settings. The results will be disseminated through open-access peer-reviewed publications, conference presentations, social media and 2-day workshops with relevant stakeholders.

Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review.

Background: Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence (AI) may help in detection and screening of dementia; however, little is known in this area. Objectives: The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method: The review followed the framework proposed by O'Malley's and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson's or Huntington's disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results: After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions: We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.