Artificial intelligence in implementation research: a scoping review of applications and recommendations

Abstract

Background

Artificial intelligence (AI) holds considerable potential for addressing challenges in knowledge translation and implementation research, including increasing the speed of knowledge synthesis, tailoring of implementation strategies, and collection of implementation data. To date, no prior review has systematically examined how AI has been applied within implementation research.

Aims

This scoping review aimed to synthesize the current evidence on applying AI in implementation research, identify reported outcomes and challenges, including ethical, regulatory, and practical considerations.

Methods

This review is reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review checklist. A systematic search was conducted across MEDLINE Complete, Scopus, IEEE Xplore, and ACM Digital Library on 31 July 2025. Seven peer-reviewed empirical studies and methodology papers were included and narratively synthesized.

Results

Included studies applied machine learning, natural language processing, and generative AI to support implementation monitoring, strategy selection, literature synthesis, and knowledge dissemination. These applications may help challenges related to speed, analytic burden, sustainability, and contextual tailoring in implementation research. Ethical considerations included privacy, transparency, and equity.

Conclusions

The current evidence base reflects an early stage of AI application in implementation research. While offering important insights, it highlights opportunities for deeper theoretical integration and more robust empirical evaluation to advance translation of research into practice.

Artificial intelligence shows promise in helping implementation research, but more work is needed to ensure these tools are used thoughtfully, fairly, and effectively.

Implications.

Practice: Practitioners may use artificial intelligence (AI) to improve implementation, through monitoring, strategy selection, evidence synthesis, and knowledge dissemination.

Policy: Policymakers should support AI applications in implementation research that extend beyond operational efficiency to understand why and how implementation strategies work.

Research: AI applications are rarely grounded in established implementation frameworks or evaluated against recognized implementation outcomes, representing a missed opportunity to advance the science.

Introduction

Despite decades of investment in generating evidence in health care and public health, a persistent challenge remains: many evidence-based health innovations are not widely adopted in routine practice [1, 2]. Implementation science aims to address this challenge by studying the factors and strategies that influence the uptake of evidence-based innovations in real-world settings [3]. The field of implementation science has expanded rapidly [4], with increasing numbers of publications [5], training programs [6], funding mechanisms [4], and emerging recognition in international policy agendas including the WHO’s Global Research Agenda on Knowledge Translation [7]. Yet this expansion has not closed the research-to-practice gap at the rate the field of implementation science hoped [2]. Studies examining the translation of clinical research into practice indicate that, as of 2009, 85% of clinical research findings were not being used in patient care, and by 2014 only about 14% had been translated into practice [8].

Central to implementation science is a commitment to theory-informed implementation research. Implementation research draws on theories, models, and frameworks to systematically characterize the contexts in which innovations are introduced, understand the mechanisms that enable their uptake, and evaluate whether implementation efforts succeed [9, 10]. These theoretical instruments provide a common language for accumulating evidence across diverse implementation settings; however, a particular challenge in implementation science is moving beyond describing “what works” to understanding why and how it works, i.e. understanding mechanisms through which implementation produce change [11]. Establishing the mechanisms and evaluating these causal pathways demands analytical approaches that are often resource-intensive and technically demanding [12]. Furthermore, implementation scientists and practitioners are challenged to keep up with the volume of implementation information and to tailor evidence-based strategies to varied contexts with precision and efficiency [2]. As Chambers and Emmons observe, the “adolescence” of implementation science has led to an explosion of new models and data that can be difficult to synthesize and utilize efficiently [13].

There is a growing recognition that artificial intelligence (AI) has the potential to help address these challenges of information synthesis, analytic burden, and contextual tailoring in implementation research [14]. AI refers to computational systems capable of performing tasks that traditionally require human intelligence, including learning from data, interpreting language, and making predictions or decisions [15]. Within this broader field, several AI technologies are particularly relevant to implementation science. Machine learning (ML) approaches enable algorithms to learn patterns from large datasets and generate predictive models that can support decision-making or identify determinants of implementation outcomes [16, 17]. Natural language processing (NLP) methods allow automated analysis and synthesis of large volumes of unstructured text, including research literature, clinical notes, or qualitative data, there by supporting evidence synthesis and knowledge extraction [18]. More recently, generative AI systems based on large language models (LLMs), such as ChatGPT, have demonstrated capabilities in summarizing evidence, drafting reports, and supporting communication with diverse stakeholders, potentially reducing the cognitive and administrative burden associated with implementation work [19].

Reflecting growing interest in the potential of AI, Trinkley et al. outline six major challenges in implementation research that AI might address: speed, sustainability, equity, generalizability, context–outcome relationships, and causality. For example, AI could accelerate labor-intensive data collection (thus increasing speed) and analysis, help tailor interventions to local contexts (improving relevance and generalizability), reveal complex patterns or mechanism insights in implementation data (advancing understanding of context-outcome relationships and causality), and potentially do so in a cost-effective manner (promoting sustainability) [14]. Younas and Reynolds similarly argue that AI can augment various stages of the implementation process, including planning (e.g. needs assessments through predictive analytics), developing implementation tools and guidelines, tailoring implementation strategies, and ongoing monitoring and evaluation [8].

While the above opinion pieces make a compelling case for the promise of AI in implementation research, to our knowledge, no completed prior review has systematically examined the literature on AI applications in implementation research. A protocol for a living scoping review on this topic has recently been developed [20], reflecting growing recognition of AI application as a field priority. In response, we conducted a scoping review to answer the following questions:

1. How has AI been applied in implementation research?

2. What reported outcomes, benefits, or challenges have emerged from the use of AI in implementation research?

3. What are the ethical, regulatory, or practical considerations sicussed in applying AI in implementation research?

Methods

This scoping review was conducted in accordance with the Arksey and O’Malley’s framework [21] for scoping reviews and is reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review (PRISMA-ScR) checklist [22] (Supplementary File S1). A review protocol was developed a priori but was not registered; the protocol is available from the authors upon request. A scoping review methodology was chosen because the use of AI in implementation research is an emerging area, and the review aimed to characterize the breadth of evidence and identify gaps in the available literature.

Eligibility criteria

To guide eligibility decisions and ensure conceptual coherence, it is important to distinguish three functionally distinct roles that AI may play in the context of implementation research. First, AI may function as the innovation being implemented: for example, an AI-driven chatbot delivering a behavioral health program, or a clinical decision-support algorithm generating diagnostic recommendations. In these cases, AI is the evidence-based innovation whose adoption is the object of study. Second, AI may function as a tool to support implementation processes: for example, ML used to monitor intervention fidelity, NLP used to identify implementation barriers from clinician interview data, or predictive models used to match strategies to context. Here, AI augments the work of implementation practitioners or researchers but remains distinct from the innovation being implemented. Third, AI may function as a methodological tool to support implementation research itself: for example, topic modeling to map the growth of the implementation science literature, or LLMs to synthesize qualitative expert discussions. This review focuses on the second and third roles—AI deployed to support implementation processes and to advance implementation research methodology—rather than on AI as the clinical innovation itself. This distinction has direct implications for eligibility decisions and for interpreting the reported benefits and challenges of AI in this context.

Studies were included in the review if they met all the following criteria: (i) empirical studies and methodology papers reporting the use of AI methods to directly support implementation processes (e.g. identifying implementation barriers, selecting implementation strategies) or to support implementation research methodology (e.g. evidence synthesis, qualitative data analysis). Studies where AI served solely as the health intervention being implemented, with no examination of the implementation process itself, were not eligible; (ii) peer-reviewed studies that described, evaluated, or discussed the use of AI in implementation science, including reported outcomes, benefits, challenges, or ethical, regulatory, or practical considerations; and (iii) studies published in English. Studies could be conducted in any discipline or setting.

Studies were excluded if they met any of the following criteria: (i) studies in which AI functioned solely as the clinical intervention or diagnostic tool, with no examination of how that AI system was implemented, what implementation strategies were used, or what implementation processes or outcomes were observed; (ii) studies reporting AI for clinical decision support or diagnosis in which the term “implementation” referred only to technical deployment or software installation, with no engagement with implementation science concepts; (iii) publication types that did not report primary or secondary empirical data, including editorials, commentaries, and opinion pieces; (iv) conference abstracts and theses; and (v) studies published in languages other than English.

Search and information sources

A comprehensive search strategy was developed in collaboration with a health sciences librarian and the coinvestigator team. The search strategy combined terms related to “artificial intelligence” (including machine learning, natural language processing, generative AI, large language models, and intelligent systems) with terms related to “implementation research” (including implementation science, dissemination science, knowledge translation, and translational research). The search was conducted across four electronic databases: MEDLINE Complete (via EBSCOhost), Scopus, the Institute of Electrical and Electronics Engineers (IEEE) Digital Library, and Association for Computing Machinery (ACM) Digital Library published from 1 January 2018 to 31 July 2025. MEDLINE Complete was chosen to ensure methodological rigor through its strict journal selection criteria and controlled Medical Subject Headings indexing, enabling highly reproducible search strings. To maximize overall search recall, Scopus was integrated due to its broad multidisciplinary coverage, allowing for capture studies across implementation settings. Gray literature sources were not systematically searched, consistent with the focus on peer-reviewed literature. The full search strategy is provided in Supplementary File S2.

Study selection

The study selection process was managed via Covidence and followed a multistage screening protocol within a team with complementary expertise in implementation science (J.M., H.K., A.C.) and AI (H.S., Y.Z.):

• Stage 1: Title and abstract screening. All imported citations were screened independently by two reviewers (J.M. and H.S.) against the initial eligibility criteria.

• Stage 2: Preliminary full-text review. Articles identified as potentially relevant moved to full-text assessment. Two reviewers (J.M. and H.S.) independently evaluated these articles for inclusion.

• Stage 3: Calibration and criteria refinement. To ensure consistency and minimize selection bias, the full review team (J.M., H.S., Y.Z., A.C., H.K.) conducted an iterative reflection session on a subsample of 12 full-text articles. This served as a “pilot” to identify nuances not captured in the initial criteria.

• Stage 4: Final full-text assessment. Following the team’s reflection, the eligibility criteria were formally refined. All full-text articles were then reassessed against these updated criteria to ensure the final selection was robust.

At every stage, reviewers documented reasons for exclusion. Any disagreements between independent reviewers or “unclear” designations were resolved through discussions among the wider review team.

Data extraction

The review team developed and piloted a data extraction form designed to capture key variables across multiple domains. These domains included (i) study characteristics (author(s), publication year, title, source/journal, country or region, study design); (ii) AI applications (the type or method of AI used, its application domain, and any tools or platforms employed, data source); (iii) implementation research characteristics, including an implementation framework or model applied, challenges addressed as outlined in Trinkley et al. (including speed, sustainability, equity, generalizability, context–outcome relationships, and causality), and strategies/outcomes reported. H.S. and J.M. independently extracted data from all included studies [14]; (iv) Implementation stage addressed, mapped to the Exploration, Preparation, Implementation, Sustainment (EPIS) framework [23] to enable mapping of where in the implementation process AI is being applied. To ensure accuracy, the remaining members of the review team verified the extracted data for two studies each against the original sources. Any discrepancies identified during this process were resolved through discussion and consensus.

Data synthesis

A narrative synthesis approach was used to organize and summarize the extracted data in line with the review questions. This process involved mapping findings across three domains: (i) applications of AI in implementation research, (ii) reported benefits and challenges, and (iii) ethical, regulatory, and practical considerations.

Data synthesis was conducted independently by J.M. and H.S. All extracted data were collated and managed in Microsoft Excel. The initial phase focused on descriptive characteristics, including study design, AI applications, and implementation settings, to provide an overview of how AI has been applied. In the subsequent phase, a framework-guided synthesis was undertaken for selected domains relevant to implementation science, drawing on established constructs including implementation outcomes [24] and the six challenge domains outlined by Trinkley et al. [14].

Results

The database searches identified a total of 2995 records. After removal of 1306 duplicates, 1689 unique records were screened at the title and abstract stage. Of these, 1502 were deemed ineligible, and 187 records proceeded to full-text review. Seven studies met all inclusion criteria and were included in the scoping review. Figure 1 presents the PRISMA-ScR flow diagram illustrating the study selection process.

Figure 1.

PRISMA-ScR flow diagram of the study selection process.

Study characteristics

The characteristics of the seven included studies are summarized in Table 1. The studies were published between 2019 and 2025. Six of the seven studies were conducted in or included participants from high-income countries. Study designs were heterogeneous, encompassing methods development papers, platform descriptions, and mixed-methods evaluations.

Table 1.

Characteristics of included studies.

Author (year)	Country/Region	Program/Implementation setting	Study design	Population	Implementation frameworks or outcomes reported	Implementation stage (EPIS)
Berkel et al. (2019) [25]	United States	Community delivery of a parenting program (New Beginnings Program)	Methods development with empirical illustration	Program participants and delivery staff	Implementation frameworks: Taxonomy by Durlak and DuPre for implementation components (Durlak and DuPre [34] Implementation outcomes: fidelity, acceptability	Implementation
Bishara et al. (2022) [26]	United States	Preoperative/anesthesia clinical setting	Platform description and technical feasibility study	Clinicians and supporting IT teams	Not explicitly reported	Sustainment
Desmedt et al. (2025) [27]	Europe/Global	Higher education (research)	Mixed-methods evaluation	Researchers	Not explicitly reported	Sustainment
Shin et al. (2025) [28]	United States	Higher education (research)	Qualitative study	Design practitioners	Not explicitly reported	Implementation
Moussa et al. (2021) [29]	Australia	Community pharmacy	Mixed methods evaluation	Delivery staff	Implementation framework: Consolidated Framework for Implementation Research (CFIR) and Theoretical Domains Framework (TDF) Implementation outcome: Not explicitly reported	Implementation
Rinne et al. (2024) [30]	United States	Health care (use of electronic health records)	Qualitative study	Researchers	Not explicitly reported	Implementation
Scaccia and Scott (2021) [31]	Global	Academic literature synthesis (no specific setting)	Bibliometric content analysis	No human participants	Not explicitly reported	Exploration

Abbreviation: EPIS, Exploration, Preparation, Implementation, and Sustainment framework [23].

Berkel et al. [25] and Scaccia and Scott [31] were methods-focused, applying ML/NLP to implementation-related data and demonstrating feasibility. Moussa et al. [29] and Shin et al. [28] reported empirical evaluations. Moussa et al. used data from a 2-year implementation project to train and test an ML model, while Shin et al. evaluated the effectiveness of an AI tool. Bishara et al. [26] described a platform development study that included a retrospective technical evaluation using a clinical dataset. Rinne et al. [30] documented the use of LLM to summarize a qualitative expert panel discussion, with participant feedback gathered on the process. The study of Desmedt et al. [27] was a mixed-methods evaluation of an AI-generated tool for knowledge dissemination.

Two studies explicitly reported using implementation frameworks. Moussa et al. [29] and Berkel et al. [25] used the Consolidated Framework for Implementation Research (CFIR) [32] and Theoretical Domains Framework (TDF) [33] to categorize identified implementation barriers. Berkel et al. [25] referenced a “multidimensional model of implementation” and drew on the taxonomy by Durlak and DuPre [34] for implementation components. Only one study explicitly reported measuring implementation outcomes, assessing fidelity and acceptability and linking these to parent and child outcomes [25].

Regarding implementation stage, four studies [25, 28–30] addressed activities consistent with the implementation phase of the EPIS framework (active delivery monitoring and strategy selection) [23]; two studies [26, 27] addressed sustainment-relevant functions (building learning mechanisms into clinical workflows; dissemination beyond academic audiences); and one study [31] addressed exploration-phase activities (evidence synthesis).

Application of artificial intelligence: benefits, outcomes, and challenges

Across the seven included studies, AI applications fell into four broad categories: (i) implementation monitoring and feedback, (ii) data-driven strategy selection and contextual tailoring, (iii) literature and qualitative data synthesis, and (iv) knowledge dissemination. Table 2 summarizes the specific AI applications, key findings, and reported benefits and challenges within each category.

Table 2.

Applications of artificial intelligence in included studies, including their functions, benefits, challenges, and ethical, regulatory and practical considerations.

Author (year)	AI applications	Primary functions of AI in implementation research	Implementation science challenges addressed (as outlined in Trinkley et al. [14])	Key findings	Reported benefits and challenges	Ethical, regulatory, or practical considerations
Berkel et al. (2019) [25]	Supervised ML and NLP (computational linguistics and automated speech analysis) applied to session-level implementation data	Automated monitoring of implementation fidelity, real-time feedback for quality delivery, and participant responsiveness	Speed, sustainability High burden, cost, and subjectivity of traditional fidelity assessment; limited scalability of observational coding	AI-derived fidelity and quality indicators demonstrated predictive validity, showing associations with parenting and child outcomes	Benefits: Reduced data collection burden; scalable, near–real-time feedback to facilitators Challenges: Models require large, high-quality training datasets; AI outputs still required interpretation by supervisors	Risk of overreliance on automated fidelity scores; need for transparency in how AI-derived indicators are generated; importance of human oversight to contextualize AI feedback
Bishara et al. (2022) [26]	ML models (supervised and unsupervised) embedded within a full-stack implementation science platform that enables predictive modeling and data mining (“Opal” platform)	Integration of AI into clinical workflows for prediction, monitoring, and decision support	Speed Translational gap between AI model development and real-world clinical implementation; lack of infrastructure for sustained deployment	The platform enabled development and validation of predictive models (e.g. acute kidney injury) with strong performance and rapid iteration	Benefits: Supports learning health systems; facilitates continuous model updating and evaluation Challenges: Focused primarily on technical feasibility rather than implementation outcomes (e.g. adoption, acceptability)	Regulatory oversight for clinical AI tools; data governance and security; need for clinician trust and interpretability of predictions
Desmedt et al. (2025) [27]	Generative AI (NotebookLM with voice synthesis) for automated podcast creation summarizing research articles	Dissemination of research findings through AI-generated media to broader audiences (health professionals, patients, public)	Speed, generalizability Limited reach and accessibility of traditional academic dissemination formats	AI-generated podcasts were perceived as engaging and understandable; many authors assumed hosts were human	Benefits: Increased accessibility and engagement; scalable dissemination strategy Challenges: Minor factual inaccuracies, mispronunciations, and loss of nuance	Ethical concerns around transparency; risk of misinformation; need for human review prior to dissemination
Moussa et al. (2021) [29]	Supervised ML (random forest models) applied to data mining	Predicting which implementation strategies are most effective for specific barriers	Generalizability, assessing context Difficulty tailoring implementation strategies to context; reliance on intuition rather than empirical matching	ML models predicted successful strategy–barrier matches with high accuracy (∼97%)	Benefits: Data-driven tailoring of implementation strategies; potential to accelerate implementation success Challenges: Requires consistent barrier coding and sufficient historical data	Risk of algorithmic bias if training data reflect narrow contexts; importance of aligning AI outputs with established implementation frameworks
Rinne et al. (2024) [30]	LLM (ChatGPT) for summarizing qualitative expert panel discussions	Rapid synthesis and reporting of qualitative implementation knowledge	Speed Time- and labor-intensive qualitative analysis; delays in knowledge translation	AI-generated summaries efficiently captured high-level themes but lacked nuance and contextual depth	Benefits: Significant time savings; useful for preliminary synthesis Challenges: Required human editing; missed subtle or dissenting perspectives	Risks of oversimplification and “flattening” of qualitative data; accountability for AI-generated interpretations
Scaccia and Scott (2021) [31]	NLP and topic modeling (LDA) for automated content analysis of academic literature	Meta-research and large-scale synthesis of implementation science literature	Speed Difficulty synthesizing large bodies of implementation research over time	NLP identified dominant topic clusters and shifts in the field over 15 years	Benefits: Efficient overview of field trends; supports strategic reflection Challenges: Abstract-level analysis limits depth; interpretive validation still required	Transparency in model assumptions; potential misclassification of complex concepts; AI complements rather than replaces expert synthesis
Shin et al. (2025) [28]	LLM to customize translational “design cards” (research summary artifacts) for users	Contextual tailoring of research and implementation guidance	Generalizability, equity Poor fit of generic tools to local implementation contexts	LLM-powered tools improved perceived relevance and usability of design resources	Benefits: Enhanced contextualization; supports user-centered implementation planning Challenges: Variable output quality depending on prompts and data	Need for safeguards against hallucination; ensuring equity and inclusivity in AI-generated guidance

Abbreviations: AI, artificial intelligence; LDA, latent Dirichlet allocation; LLM, large language model; LM, language model; ML, machine learning; NLP, natural language processing.

Implementation monitoring and feedback

Two studies applied AI to automate the collection and interpretation of implementation data in near-real time. Berkel et al. [25] used supervised ML and NLP applied to session audio recordings to monitor implementation fidelity and participant responsiveness, reducing the time and subjectivity of traditional human coding while generating feedback for facilitators and supervisors. Bishara et al. [26] described the development of an ML platform (Opal) embedded within a perioperative clinical setting, enabling continuous data extraction, predictive modeling, and decision support linked to routine electronic health records (EHRs). Both applications addressed the challenge of speed identified by Trinkley et al. [14], and Bishara et al. [26] further addressed sustainability by building learning mechanisms into routine workflows.

Data-driven strategy selection and contextual tailoring

Two studies used AI to match implementation strategies to context. Moussa et al. [29] trained a random forest model on data from a two-year implementation project to predict which strategies were most effective for specific barriers identified through CFIR and TDF, achieving approximately 97% accuracy. Shin et al. [28] used an LLM to customize translational design cards to the specific needs of design practitioners, improving the perceived relevance and usability of implementation guidance. These applications addressed the challenges of generalizability and context identified by Trinkley et al. [14], by advancing precision implementation approaches that reduce reliance on generic, one-size-fits-all strategy recommendations.

Literature and qualitative data synthesis

Two studies applied AI to manage and synthesize large bodies of implementation-relevant text. Scaccia and Scott [31] used NLP and topic modeling (latent Dirichlet allocation) to conduct a bibliometric content analysis of the implementation science literature over 15 years, identifying dominant topic clusters and shifts in the field at a scale not feasible through manual review. Rinne et al. [30] applied an LLM (ChatGPT) to summarize qualitative expert panel discussions, generating thematic summaries in significantly less time than conventional qualitative analysis, though with noted loss of nuance and contextual depth. Both applications primarily addressed the challenge of speed.

Knowledge dissemination

Desmedt et al. [27] evaluated a generative AI tool (Google NotebookLM) that automatically converted published journal articles into audio podcasts for dissemination to broader audiences including health professionals and the public. AI-generated podcasts were generally perceived as engaging and accessible, with many listeners unable to identify the content as AI-generated; however, minor inaccuracies, tone mismatches, and credibility concerns were noted. This application addressed the challenges of speed and generalizability, with potential to extend the reach of implementation evidence beyond academic audiences.

Despite reported benefits, the authors of several included studies described potential challenges. Dependence on data quality and completeness was a recurring concern, with risks to model performance and generalizability when AI systems were trained in narrow or context-specific settings [26, 29]. Loss of nuance and contextual depth was noted particularly in applications involving qualitative or generative content [27, 30]. User trust and acceptability varied, with concerns about overreliance on automated outputs and resistance from practitioners whose work was being monitored or augmented [25, 27]. Notably, no study applied AI to address the challenges of causality or mechanism testing identified by Trinkley et al. [14].

Ethical, regulatory, and practical considerations

Three broad categories of ethical, regulatory, and practical considerations were identified across the included studies: (i) privacy and data governance, (ii) transparency and human oversight, and (iv) equity and access.

Privacy and data governance

Concerns about privacy and data governance were most prominent in studies involving the collection of sensitive personal data, including recorded therapeutic sessions and EHRs [25, 26]. Identified safeguards included informed consent processes for session recording, access controls, deidentification protocols, and audit trails.

Transparency and human oversight

Transparency in data collection and processing was a cross-cutting concern across multiple AI types, including complex ML models [26, 29] and LLMs [27, 30]. In both ML models and LLMs, the internal logic of the model was not readily interpretable, raising challenges for accountability, practitioner trust, and uptake. Several authors emphasized the importance of maintaining human oversight and positioning AI as a decision-support tool rather than an autonomous decision-maker. Desmedt et al. [27] additionally raised the importance of transparency about the AI-generated nature of dissemination content, given that many listeners were unable to identify podcasts as AI-produced.

Equity and access

Equity considerations were noted across several studies but were not directly examined in any. Authors acknowledged the potential for AI systems to reproduce or exacerbate disparities when trained on unrepresentative data [29], or when deployed unevenly across resource-rich and resource-constrained settings [25, 27]. Shin et al. [28] presented equity as a dual consideration: a rationale for developing AI-tailored implementation tools for underresourced practitioners, and simultaneously a risk if access to those tools remained unequal. Practical challenges related to integration and sustainability were also evident: embedding AI tools into existing workflows required alignment with organizational systems, technical infrastructure, and user routines, with risks of poor uptake if tools were perceived as burdensome or misaligned with practice [25, 26].

Discussion

This scoping review sought to explore how AI has been applied in implementation research, including the benefits, outcomes, challenges and future considerations. Building on recent conceptual work [14], our findings indicate that AI has been used to support implementation research by addressing persistent operational challenges related to speed, analytic burden, sustainability, and contextual tailoring. The application of AI in implementation research appears early in maturity relative to disciplines such as oncology and radiology, where AI has been routinely deployed as a tool to advance clinical efficiency [35].

Across the included studies, AI was often used to support feedback, dissemination, facilitation, and tailored communication strategies. This aligns with broader observations in digital health and learning health systems, where AI is most often used to enhance data processing, prediction, and decision support [15]. ML and NLP approaches reduced the time and labor required for data synthesis and monitoring, while generative AI expanded the reach and accessibility of implementation knowledge. These applications map closely onto several of the challenges outlined in Trinkley et al. [14], particularly those related to speed, sustainability, and generalizability. These findings provide empirical support for claims that AI can help implementation science “keep up” with its own growth [36]. However, the absence of applications addressing causality and mechanism testing suggests that AI is currently being used to optimize existing workflows rather than to advance explanatory aims of the field [12]. Among the studies included, AI applications were concentrated in the implementation phase of the EPIS framework [23], with limited evidence relating to exploration, preparation, and sustainment. This suggests that the potential of AI to support preimplementation decision-making (e.g. using predictive models to assess implementation readiness or assess implementation context) may have been unexplored.

Despite implementation science’s emphasis on theory-informed research, relatively few studies explicitly integrated AI applications with established implementation theories, frameworks or outcomes. Only two studies reported the direct use of frameworks such as CFIR [32] or TDF [33], and only one study reported measuring implementation outcomes [25]. This mirrors broader critiques that digital and AI-enabled innovations are often introduced into implementation contexts without sufficient grounding in implementation theory [37, 38]. Without explicit linkage to available theories, models or frameworks, it remains difficult to interpret how AI influences implementation processes and outcomes. For example, Berkel et al. [25] reported fidelity that can be contextualized within established implementation research literature to explore fidelity–outcome relationship, potentially contributing to the improvement of implementation process.

Ethical, regulatory, and practical considerations were acknowledged across studies. Consistent with a broader pattern in AI research [39–41], privacy and transparency were recurring concerns, particularly in studies involving recorded interactions or health data. The emphasis on maintaining human oversight aligns with emerging consensus that AI should augment rather than replace professional judgment, especially in complex social and organizational systems such as implementation contexts [42, 43]. The breadth of data types involved in included studies (i.e. audio recordings, clinical records, and text from implementation data) points to the need for data governance frameworks that extend beyond standard research ethics to address the ongoing, operational nature of AI-enabled monitoring in real-world implementation settings. Although the small number of studies precluded formal subgroup analyses, ethical and practical considerations appeared to vary according to AI types and application contexts. Predictive and clinical decision-support applications raised greater concerns regarding privacy, algorithmic bias, interpretability, accountability and human oversight, particularly when applied to patient-level data. Generative AI applications raised additional concerns regarding output accuracy, provenance, transparency of AI involvement, and the need for human verification.

Equity considerations were frequently noted across the included studies, reflecting a well-recognized risk that AI systems can reproduce or exacerbate health inequities when trained on biased or unrepresentative data, or when deployed unevenly across resource-rich and resource-constrained settings [41, 44]. It is important, however, to distinguish between two distinct ways in which AI is being used in implementation research. In some studies, AI functioned as the primary intervention: for example, a chatbot delivered a behavioral support program, or an algorithm generated personalized treatment recommendations. In these cases, the equity concern centers on whether the AI-driven intervention performs equivalently across population subgroups, and whether access to AI-delivered care is equitably distributed. In other studies, AI was used to support the implementation process itself: for example, NLP was used to identify barriers from clinician interviews, or ML was used to monitor fidelity to a clinical protocol. In these cases, equity concerns whether AI-enabled implementation support reaches and benefits underresourced settings as much as well-resourced ones, and whether AI tools for implementation monitoring are valid across diverse cultural and linguistic contexts. Implementation science has a strong tradition of attending to context, adaptation, and equity [45, 46], positioning it well to advance more equitable approaches to both types of AI use. Practically, this could involve conducting systematic bias audits, stratifying AI model performance by race/ethnicity, socioeconomic status, language, or geographic context. It could also involve codesigning AI tools with communities most affected by implementation gaps, drawing on participatory methods well established in implementation science [47]. Community advisory structures, plain-language explainability tools, and transparent reporting of differential performance across subgroups all represent concrete mechanisms through which equity can be operationalized in AI-enabled implementation research.

Taken together, these findings suggest four future directions or priorities for using AI in implementation research, as summarized in Table 3.

Table 3.

Summary of key findings and future directions for using AI in implementation research.

Scoping review aim	Summary of findings	Future directions or priorities for using AI in implementation research
How has artificial intelligence been applied in implementation research?	AI has primarily been applied to support feedback, dissemination, facilitation, monitoring, and tailored communication ML and NLP approaches reduced analytic burden and time demands, while Gen AI expanded access to implementation evidence	Deliberately embed AI applications within implementation frameworks (e.g. CFIR, TDF) to clarify mechanisms of action and guide theory-informed evaluation Explicitly examine how AI-enabled tools interact with established implementation strategies
What reported outcomes, benefits, or challenges have emerged from the use of AI in implementation research?	Reported benefits included improved speed, efficiency, scalability, and perceived usefulness Most studies focused on technical performance, feasibility, and acceptability. Only one study reported implementation outcome data. Challenges included limited generalizability and lack of outcome-focused evaluation	Assess impacts on core implementation outcomes
What ethical, regulatory, or practical considerations have been discussed in applying AI to implementation research?	Privacy, transparency, explainability, data governance, and the need for human oversight were frequently acknowledged. Equity risks related to biased data and uneven deployment across settings were commonly noted	Treat governance and transparency as core components of AI-enabled implementation research Integrate equity-focused frameworks to ensure AI systems promote, rather than undermine, equitable implementation

Strengths and limitations

This scoping review represents the first synthesis of AI applications specifically within implementation research in health, capturing a diverse range of AI methodologies and implementation contexts. By explicitly mapping findings to key implementation science challenges outlined by Trinkley et al., the review provides a structured lens for interpreting emerging evidence. Furthermore, the systematic approach to data extraction and narrative synthesis enabled detailed comparisons across heterogeneous study designs, AI methods, and settings, enhancing the depth and rigor of the analysis. However, certain limitations should be acknowledged. As the use of AI in implementation research is an emerging area, the small number of eligible studies constrain the breadth of conclusions that can be drawn. Our search strategy and eligibility criteria that require a direct connection with implementation research and processes may have reduced retrieval of relevant applications described using terminology unrelated to implementation science. This approach was important for maintaining the scope of the review and avoiding studies in which AI itself was the implementation target. The exclusion of non-English publications may have also resulted in the omission of relevant research from non-English-speaking contexts.

Conclusion

AI holds considerable promise for addressing long-standing operational challenges in implementation research and implementation science more broadly, particularly those related to speed, sustainability, and analytic burden. The current evidence base reflects an early stage of AI application, offering important insights while also highlighting opportunities for deeper theoretical integration and more robust empirical evaluation.

Funding sources

This study was funded by Deakin Institute for Physical Activity and Nutrition Seed Fund. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Author contributions

J.M. conceptualized the review and developed the review protocol with assistance from H.K. and Y.Z. J.M. led the search strategy development in collaboration with a health sciences librarian. J.M. and H.S. independently screened titles and abstracts and conducted full-text review. J.M., H.S., Y.Z., A.C., and H.K. participated in the calibration and criteria refinement. J.M. and H.S. independently extracted data from all included studies. Y.Z., A.C., and H.K. verified extracted data against original sources. J.M. and H.S. conducted the narrative synthesis. J.M. drafted the manuscript. H.K. provided senior supervision and mentorship. All authors contributed to critical revision of the manuscript for important intellectual content and approved the final version for submission.

Supplementary material

Supplementary material is available at Translational Behavioral Medicine online.

Human rights

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

This study does not involve human participants and informed consent was therefore not required.

Welfare of animals

This article does not contain any studies with animals performed by any of the authors.

Transparency statement

Study registration

This study was not formally registered.

Analytic plan preregistration

The analysis plan was not formally preregistered.

Analytic code availability

There is no analytic code associated with this study.

Materials availability

There were no materials used to conduct the study.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Disclosure of use of AI

During the preparation of this work, the lead author used Deakin Gem (powered by Claude Haiku 4.5) in order to check grammar and format the manuscript texts. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article, including analysis of statistical data using AI tools.