What question did this study set out to answer?

This review aims to synthesize research on AI-based methods for mitigating artefacts in radiography and assess their reporting practices.

June 10, 2026Journal of Imaging Informatics in Medicine0 citationsOpen Access

AI-Based Post-processing for Artefact Mitigation in Radiography: A Systematic Review

SNStanley A. NorrisMonash Health HMHarry MarlandFiona Stanley Hospital DSDarcy StephensonMonash Health

Key Points

This review aims to synthesize research on AI-based methods for mitigating artefacts in radiography and assess their reporting practices.
Conducted a systematic review of literature across multiple databases up to 09/01/2026.
Included original peer-reviewed studies focused on AI methods for artefact mitigation in radiography.
Reviewed studies against the CLAIM checklist to evaluate reporting quality.
Identified 10 studies out of 2965 records reviewed, covering four application categories.
Generative adversarial networks were the predominant AI architecture used.
Mean CLAIM adherence score was 17.4 ± 3.6, indicating limited methodological reporting.

Abstract

Projectional radiography is vulnerable to artefacts that can impair image quality and obscure or mimic pathology, confounding image interpretation. This systematic review synthesises published research on AI-based post-processing methods for artefact mitigation in radiography, identifies challenges to clinical translation, and evaluates reporting practices against the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Literature searches were performed across multiple bibliographic databases for studies published before 09/01/2026. A broad radiology screening strategy was used, after which studies specific to radiography were selected. Original peer-reviewed articles on AI-based post-processing methods for artefact mitigation in radiography were included. Exclusion criteria that included AI methods requiring access to pre-reconstruction data, hardware-based approaches, did not involve human subjects, and non-English languages publications. Screening was performed independently by two reviewers, and included studies were assessed against CLAIM. The review protocol was registered with PROSPERO (CRD420251089690). The search identified 2965 records, of which 10 studies were included. Four application categories were identified: bone shadow removal in chest X-rays, immobilisation device removal, electronic device removal, and general acquisition artefact removal. Studies were published between 2020 and 2025, with generative adversarial networks (GANs) the predominant architecture. No study was prospective, and methodological details were typically not sufficiently reported to allow reproducibility. CLAIM adherence was limited, with a mean score of 17.4 ± 3.6 out of 42 applicable items. The current evidence base is insufficient to support clinical adoption. Future work should prioritise external multi-institutional testing, clinically meaningful endpoints, failure mode analysis, open data and code, and workflow-embedded, prospective study design.

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