AI-Enabled Digital Literacy Support

Purpose This study aims to explore the adoption of artificial intelligence (AI) initiatives within Indian organizations, using the technology-organization-environment (TOE) framework to understand the key drivers and barriers. The primary objective is to identify the critical factors influencing successful AI integration, including technological readiness, organizational capacity and environmental pressures. Design/methodology/approach The authors conducted in-depth interviews of 26 executives of large business organizations in India. These experts had in recent times managed the digital transformation journey in their organizations based upon AI tools. A semistructured open ended interview questionnaire was used to conduct in-depth personal interviews with the experts. The data was content analyzed for themes. Findings The research study findings indicated that to increase adoption of Al based digital initiatives, it was imperative to cater to three elements of the TOE framework. Sub themes from the three themes, namely technology, organization and environment were considered to evaluate the impact of them on the adoption of AI as part of digital transformation in organizations. The investigation revealed that increase in organizational AI tools adoption could be achieved by first initiating awareness and sensitization incentives. Second, both short term and long duration training programs on machine learning and AI must be conducted. This had to be undertaken especially amongst the users to enhance the ease of use of the AI-based digital transformation tools. New organizational AI capabilities have to be built symbiotically with the legacy enterprise information systems of the organization. Dedicated information technology resources were required to be developed. The study findings also indicated that managers have to be aware of legal compliance while using AI. Furthermore, executives must comprehend the rapid change both in AI tools potency and also in customer behavior. Leadership support from top management team was also a key factor which accelerated and facilitated adoption of AI-based digital transformation initiatives. The results indicate that AI adoption depends not only on technical capabilities, but also on organizational culture and external forces. The paper concludes with practical recommendations for managers and policymakers to enhance AI adoption strategies, offers theoretical implications for future research, and highlights the need for targeted interventions to address ongoing challenges in digital transformation within the Indian context. Practical implications This research study findings would help managers leading AI enabled digital transformation initiatives in their firms. The findings would help the managers understand what went right and what could potentially go wrong during implementation of artificial intelligence initiatives in organizations. The study findings can act as a guiding principle workbook for organizations which are planning to embark on the journey of launching AI led transformation in their organizations by focusing on internal (technology and organization) and external (environment) factors. The study also sheds light on how organizations are driven by external catastrophic events like Covid-19 pandemic. The study findings will also be relevant for the change leaders of organizations to understand how to be the chief narrator of AI based digital transformation. Furthermore, it would also help executives regarding how to track AI adoption to measure success of the digital initiatives in organizations. Social implications The authors conducted an empirical investigation. The authors explicated what factors facilitated AI-based digital transformation initiatives. The study was anchored in the theoretical foundation of Technology Organization TOE framework. Thus, this research contributed to the theory of TOE in the context of AI-based digital transformation. This will assist research in studying how the various elements of technology (skills, trust on technology and knowledge on the subject), organization (readiness and support from leadership) and environment (consumer behavior, competitive pressure and government regulation) impact the adoption of AI initiatives in organizations. This study was conducted during and post Covid −19 which gave us an opportunity to study the impact of factors like change in customer behavior, accelerated initiation on adoption of digital initiatives for organizations and hence, the impact of a pandemic on firms in emerging economies while deriving the inferences. Originality/value This empirical research investigation was one of the initial studies that examined organizational AI-based digital transformation planning and execution journey using the theoretical perspectives of TOE. The need for the paper is to address gaps in existing research by exploring factors that influence the adoption of AI within Indian organizations using a qualitative approach. While the concept of TQM and AI has been researched previously, the authors aim to contribute by analyzing specific drivers, barriers, and contextual influences relevant to the Indian setting. This addresses the lack of in-depth understanding about how these factors operate in India, which is important for both theoretical and managerial implications.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

French education in the United States faces significant challenges in achieving widespread language proficiency due to traditional teaching methodologies, limited immersion opportunities, and resource constraints. Traditional classroom settings often rely on outdated approaches that fail to engage students and provide limited scope for personalized learning. Additionally, the lack of access to native speakers and authentic cultural contexts further exacerbates the difficulties in acquiring fluency. These challenges are particularly pronounced in rural and underserved areas where resources for foreign language education are scarce. Artificial Intelligence (AI) offers innovative solutions to address these persistent issues by transforming the way French is taught and learned. Through personalized learning experiences, real-time feedback, and adaptive content delivery, AI has the potential to significantly enhance language acquisition. AI-powered platforms can analyze individual learner progress and tailor lessons to suit their unique needs, fostering a more engaging and effective learning environment. Conversational agents, driven by natural language processing, simulate real-life interactions, enabling learners to practice spoken French in a low-pressure setting. Furthermore, speech recognition technologies provide instant feedback on pronunciation and help learners refined their accents, bridging the gap between classroom learning and real-world communication. This paper explores the transformative role of AI in advancing French language proficiency for students and professionals in the U.S., with a focus on key applications such as AI-powered platforms, conversational agents, and speech recognition tools. It also identifies critical challenges in the adoption of AI technologies, including accessibility issues, data privacy concerns, and the risk of over-reliance on technology. Strategies for overcoming these barriers, such as integrating AI tools into existing educational systems, training educators, and developing affordable solutions, are proposed to ensure equitable and effective implementation. The paper further highlights case studies of successful implementations, providing insights into the best practices and lessons learned. For example, platforms like Duolingo and Mondly have demonstrated how AI can enhance learner engagement and improve proficiency, while universities incorporate AI tools into their language programs have reported increased student motivation and success rates. These examples underscore the potential of AI to revolutionize French education in the U.S. by making it more accessible, efficient, and learner centered. By leveraging the capabilities of AI, French education in the U.S. can address long-standing barriers and achieve greater language proficiency among learners. This transformation not only supports individual academic and professional growth but also enhances cross-cultural communication and global collaboration. The integration of AI into French education represents a critical step toward ensuring proficiency in this globally significant language and equipping learners with the skills needed to thrive in an interconnected world.

Marketing scientists as well as practitioners believe that artificial intelligence (AI) holds the promise of productivity gains for organizations. However, there has been little scientific research into these theories. This study investigates the role of AI in enhancing marketing productivity, deriving insights from a case study conducted with the marketing team of an industrial software start-up. Drawing upon Case Study Analysis by Yin (2018) and Participatory Action Research by Kemmis and McTaggart (2007), the study employs a combination of survey interviews, AI tool research and AI tool testings. Key findings indicate that productivity gains are more likely than productivity impairments with the use of marketing AI tools. This effect is even stronger when knowledge workers possess high levels of AI skills and utilize AI tools with suitable capabilities. Having closely analyzed six marketing disciplines, particularly SEO / content and design demonstrated significant productivity gains including generative AI (GAI) tools the team already subscribed to like ChatGPT 4 and Canva, but also new AI solutions. While an AI tool’s level of integration only showed a weak positive productivity impact, future studies are suggested to further investigate this variable by comparing the effects of less advanced but more accessible tools like generative AI versus highly advanced, but less accessible business AI. Having navigated the vast and dynamic landscape of AI tools, insights further emphasize the importance of AI experience sharing and informed decision-making, implying knowledge of own user rights and always staying updated on AI advancements. Zooming out from process level, the work's literature review further highlights the role of environmental and organizational AI enablers, like budget allocation, fostering AI trust and mindset, but also implementing AI routines and responsibilities. Overall, this research underscores the imperative for companies, especially startups and SMEs, to explore AI technology as a means to enhance productivity and gain a competitive edge.

The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned

Generative artificial intelligence (AI) tools like ChatGPT hold the capacity to tremendously impact patterns of information seeking behavior in higher education, as students rely on AI tools for a variety of tasks in their daily life. This study examines the current state of how U.S. university students perceive and use AI chatbots versus traditional online search engines and academic library resources for academic information seeking and retrieval tasks. Based on an understanding of information seeking concepts drawn from existing information behavior research and theory, an electronic survey was distributed to 236 students from diverse demographic backgrounds, measuring information source use, preference, perceived relevance, and satisfaction across AI tools, search engines, and library databases. The results of the survey suggest that, while search engines like Google remain dominant for information retrieval in higher education, generative AI tools are an increasingly significant component of students’ information worlds. Younger students and international students are especially likely to use AI for academic tasks. Students who are frequent AI users also report higher satisfaction in the information supplied by AI models. These findings are indicative of a shifting ecology of information behavior where artificial intelligence serves both as a complement and a competitor to traditional information sources like search engines and university libraries, presenting important implications for information literacy instruction, academic library services, and educators navigating AI integration in higher education.

Cross-sectional survey study BACKGROUND: Artificial intelligence (AI) tools are increasingly integrated into various aspects of medicine, including medical research. However, the scope and manner in which early-career surgeons utilize AI tools in their research remain inadequately understood. This study aimed to investigate the frequency and specific applications of AI tools in medical research among early-career surgeons, including their perceptions, concerns, and outlook regarding AI in research. A survey comprising 25 questions was distributed among members of an international club of early-career spine surgeons (<10 years of experience). The survey assessed demographics, AI tool utilization, access to AI training resources, and perceptions of AI benefits and concerns in research. Sixty early-career surgeons participated, with 86.7% reporting AI tool use in their research. ChatGPT was the most frequently utilized tool, with a usage rate of 93.1%. AI tools were primarily used for grammatical proofreading (69.6%) and rephrasing (64.3%), while 26.8% of participants used AI for statistical analysis. While 80.4% perceived improved efficiency as a key benefit, 70.0% expressed concerns about reliability. None of the participants had received formal AI training, and only 15.0% had access to AI mentors. Despite these challenges, 91.6% anticipated a positive long-term impact of AI on research. AI tools are widely adopted among early-career surgeons for various research tasks, extending from text generation to data analysis. However, the absence of formal training and concerns regarding the reliability of AI tools underscore the necessity of training for AI integration in medical research. This study provides timely insights into AI adoption patterns among early-career surgeons, highlighting the urgent need for formal AI training programs to ensure responsible research practices.

BackgroundLiving evidence (LE) synthesis refers to the method of continuously updating systematic evidence reviews to incorporate new evidence. It has emerged to address the limitations of the traditional systematic review process, particularly the absence of or delays in publication updates. The emergence of COVID-19 accelerated the progress in the field of LE synthesis, and currently, the applications of artificial intelligence (AI) in LE synthesis are expanding rapidly. However, in which phases of LE synthesis should AI be used remains an unanswered question.ObjectiveThis study aims to (1) document the phases of LE synthesis where AI is used and (2) investigate whether AI improves the efficiency, accuracy, or utility of LE synthesis.MethodsWe searched Web of Science, PubMed, the Cochrane Library, Epistemonikos, the Campbell Library, IEEE Xplore, medRxiv, COVID-19 Evidence Network to support Decision-making, and McMaster Health Forum. We used Covidence to facilitate the monthly screening and extraction processes to maintain the LE synthesis process. Studies that used or developed AI or semiautomated tools in the phases of LE synthesis were included.ResultsA total of 24 studies were included, including 17 on LE syntheses, with 4 involving tool development, and 7 on living meta-analyses, with 3 involving tool development. First, a total of 34 AI or semiautomated tools were involved, comprising 12 AI tools and 22 semiautomated tools. The most frequently used AI or semiautomated tools were machine learning classifiers (n=5) and the Living Interactive Evidence synthesis platform (n=3). Second, 20 AI or semiautomated tools were used for the data extraction or collection and risk of bias assessment phase, and only 1 AI tool was used for the publication update phase. Third, 3 studies demonstrated the improvement in efficiency achieved based on time, workload, and conflict rate metrics. Nine studies applied AI or semiautomated tools in LE synthesis, obtaining a mean recall rate of 96.24%, and 6 studies achieved a mean F1-score of 92.17%. Additionally, 8 studies reported precision values ranging from 0.2% to 100%.ConclusionsAI and semiautomated tools primarily facilitate data extraction or collection and risk of bias assessment. The use of AI or semiautomated tools in LE synthesis improves efficiency, leading to high accuracy, recall, and F1-scores, while precision varies across tools.

PurposeThe purpose of the paper is to explore the rapidly evolving landscape of artificial intelligence (AI) tools in academic research, highlighting their potential to transform various stages of the research process. AI tools are transforming academic research, offering numerous benefits and challenges.Design/methodology/approachAcademic research is undergoing a significant transformation with the emergence of (AI) tools. These tools have the potential to revolutionize various aspects of research, from literature review to writing and proofreading. An overview of AI applications in literature review, data analysis, writing and proofreading, discussing their benefits and limitations is given. A comprehensive review of existing literature on AI applications in academic research was conducted, focusing on tools and platforms used in various stages of the research process. AI was used in some of the searches for AI applications in use.FindingsThe analysis reveals that AI tools can enhance research efficiency, accuracy and quality, but also raise important ethical and methodological considerations. AI tools have the potential to significantly enhance academic research, but their adoption requires careful consideration of methodological and ethical implications. The integration of AI tools also raises questions about authorship, accountability and the role of human researchers. The authors conclude by outlining future directions for AI integration in academic research and emphasizing the need for responsible adoption.Originality/valueAs AI continues to evolve, it is essential for researchers, institutions and policymakers to address the ethical and methodological implications of AI adoption, ensuring responsible integration and harnessing the full potential of AI tools to advance academic research. This is the contribution of the paper to knowledge.

Systematic literature reviews (SLRs) are essential for synthesising research evidence and guiding informed decision-making. However, SLRs require significant resources and substantial efforts in terms of workload. The introduction of artificial intelligence (AI) tools can reduce this workload. This study aims to investigate the preferences in SLR screening, focusing on trade-offs related to tool attributes. A discrete choice experiment (DCE) was performed in which participants completed 13 or 14 choice tasks featuring AI tools with varying attributes. Data were collected via an online survey, where participants provided background on their education and experience. Professionals who have published SLRs registered on Pubmed, or who were affiliated with a recent Health Economics and Outcomes Research conference were included as participants. The use of a hypothetical AI tool in SLRs with different attributes was considered by the participants. Key attributes for AI tools were identified through a literature review and expert consultations. These attributes included the AI tool's role in screening, required user proficiency, sensitivity, workload reduction and the investment needed for training. The participants' adoption of the AI tool, that is, the likelihood of preferring the AI tool in the choice experiment, considering different configurations of attribute levels, as captured through the DCE choice tasks. Statistical analysis was performed using conditional multinomial logit. An additional analysis was performed by including the demographic characteristics (such as education, experience with SLR publication and familiarity with AI) as interaction variables. The study received responses from 187 participants with diverse experience in performing SLRs and AI use. The familiarity with AI was generally low, with 55.6% of participants being (very) unfamiliar with AI. In contrast, intermediate proficiency in AI tools is positively associated with adoption (p=0.030). Similarly, workload reduction is also strongly linked to adoption (p<0.001). Interestingly, if expert proficiency is needed for the AI, authors with more scientific experience in their profession are less likely to adopt AI (p=0.009). However, more experience specifically with SLR publications increases AI adoption likelihood (p=0.001). The findings suggest that workload reduction is not the only consideration for SLR reviewers when using AI tools. The key to AI adoption in SLRs is creating reliable, workload-reducing tools that assist rather than replace human reviewers, with moderate proficiency requirements and high sensitivity.

Cochrane Evaluation of (Semi-) Automated Review Methods (CESAR): Protocol for an adaptive platform study within reviews.

The "Digital Literacy Program: Daily Life with AI Tools" is a community service initiative aimed at enhancing digital literacy by integrating artificial intelligence (AI) tools into daily routines. Conducted at Rumah Pertubuhan Masyarakat Indonesia (PERMAI) in Pulau Pinang, Malaysia, this program seeks to democratize access to AI technologies, fostering a foundational understanding that bridges the gap between complex AI concepts and their practical applications in everyday life. By equipping participants with the skills to utilize AI tools effectively, the program not only improves efficiency in personal and professional activities but also empowers individuals with the knowledge to navigate the evolving digital landscape. The innovative approach of this program is its focus on making AI accessible to a broader audience, promoting digital inclusivity and literacy. Through hands-on workshops and real-world applications, participants learn to integrate AI into tasks such as time management, data organization, and problem-solving, leading to enhanced productivity and informed decision-making. This initiative ultimately contributes to the broader goal of fostering a digitally literate society capable of leveraging emerging technologies for personal and collective advancement.

Purpose This narrative review investigates the interplay between gender bias in artificial intelligence (AI) systems and the potential of digital literacy to empower women in technology. By synthesising research from 2010 to 2024, the study examines how gender bias manifests in AI, its impact on women’s participation in technology, and the effectiveness of digital literacy initiatives in addressing these disparities. Purpose A systematic literature search was conducted across major academic databases, including Web of Science, Scopus, IEEE Xplore, and Google Scholar. The review focused on peer-reviewed articles, reports, and case studies published between 2010 and 2024 that addressed gender bias in AI, women’s participation in technology, and digital literacy initiatives. A thematic analysis framework was employed to identify and synthesise recurring themes and patterns. Purpose The findings reveal systemic gender biases embedded in AI applications across diverse domains, such as recruitment, healthcare, and financial services. These biases stem from factors including the under-representation of women in AI development teams, biased training datasets, and algorithmic design choices. Digital literacy programs emerge as a promising intervention, fostering a critical awareness of AI bias, encouraging women to pursue AI careers, and catalysing growth in women-led AI projects. Purpose Although gender bias in AI poses significant challenges, this review highlights digital literacy as a transformative tool for achieving gender equity in AI development and application. The study highlights the importance of inclusive AI design, gender-responsive education policies, and sustained research efforts to mitigate bias and promote equity.

Primary health care (PHC) is critical for delivering accessible and continuous care but faces persistent challenges such as workforce shortages, administrative burden, and rising multimorbidity. Artificial intelligence (AI) has the potential to support PHC by enhancing diagnosis, workflow efficiency, and clinical decision-making. However, existing research often overlooks how AI tools function within the complex realities of primary care and how clinicians and patients experience them. This scoping review maps the landscape of AI applications in PHC, with a focus on empirical studies involving direct engagement from PHC stakeholders. The review emphasizes real-world settings, clinical workflows, and the alignment of AI tools with the values and complexity of generalist care. Following Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, we searched PubMed, Web of Science, and Scopus databases up to April 13, 2024. Inclusion criteria were empirical, peer-reviewed studies published in English between January 2010 and April 2024, involving direct stakeholder interaction (general practitioners, nurses, or patients) in real-world PHC settings, evaluating AI applications (eg, diagnostics, workflow optimization, and documentation). Exclusions included algorithm-only validations, pediatric populations, secondary or tertiary care contexts not explicitly addressing PHC workflows, nonempirical research (eg, editorials or protocols), and non-English studies. We used thematic analysis to synthesize findings related to study aims, AI applications, and stakeholder roles. Of 5224 identified records, 73 studies met the inclusion criteria. Studies were grouped into four main themes: (1) early intervention and decision support (n=21; 29%), (2) chronic disease management (n=16; 22%), (3) operations and patient management (n=12; 16%), and (4) acceptance and implementation experiences (n=24; 33%). AI tools frequently demonstrated strong technical accuracy, particularly in diagnostic decision support. However, implementation in routine practice was often limited by usability barriers, workflow misalignment, trust concerns, equity gaps, and financial constraints. Overall, AI holds significant potential to support PHC, especially when aligned with clinical reasoning, workflow needs, and relational care models. However, persistent implementation barriers such as usability challenges, training gaps, and workflow integration issues must be addressed. The evidence included in this review is limited by heterogeneity in study design and the predominance of small-scale feasibility studies. Future research should prioritize pragmatic trials, co-design with PHC professionals, and anticipatory planning using future methods to ensure responsible and equitable implementation.