Facilitators and barriers to implementing AI-assisted diagnostic systems in primary healthcare services in low-resource settings: a mixed-methods study protocol.

INTRODUCTION: Although AI-assisted diagnostic systems are increasingly deployed in primary care globally, evidence from multiple countries indicates that deployment does not necessarily ensure clinical uptake. Resource-constrained and dispersed regions face implementation challenges, yet the mechanisms remain unclear. This mixed-methods protocol employs the Consolidated Framework for Implementation Research (CFIR) to systematically identify barriers and facilitators to AI implementation in primary care in such settings.

METHODS AND ANALYSIS: This study adopts a sequential mixed-methods design. Phase I involves semi-structured in-depth interviews with multi-level stakeholders (defined by the 7P principle: Patients, Providers, Purchasers, Payers, Public, Policymakers, and Product suppliers), including primary care clinicians, facility managers, external administrators, and vendor staff. Purposive and maximum-variation sampling will cover diverse geographic areas and usage levels (high, low, and non-use). The interview guide will be developed based on the CFIR 2.0 domains. Interviews will be audio-recorded, transcribed verbatim, thematically analysed, and mapped to CFIR 2.0 constructs. Phase I will recruit professional stakeholders and patients until information saturation is reached. Phase II comprises a cross-sectional survey of primary care patients targeting a minimum of 482 valid responses, to identify and quantify patient-level acceptance of AI-assisted diagnosis and its influencing factors, informed by patients' perspectives identified in Phase I. Phase III integrates findings from the first two phases and applies the Behaviour Change Wheel (BCW) and Implementation Mapping to formulate context-sensitive optimisation strategies.

ETHICS AND DISSEMINATION: This study has been approved by the Medical Ethics Committee of Inner Mongolia Medical University (approval number: YKD202602099). All participants will provide written informed consent prior to data collection. Findings will be disseminated through peer-reviewed journal publications, national and international conference presentations, and policy briefs submitted to local health authorities. Beyond the pastoral regions of China, this protocol aims to generate an evidence base and a replicable methodological template (integrating CFIR 2.0, COM-B, and Implementation Mapping) that may inform the scaled implementation of AI in comparable resource-constrained and geographically dispersed health systems. The transferability of the findings to settings such as those in Central Asia, sub-Saharan Africa, and Small Island Developing States will be explicitly assessed as a research question rather than assumed.