Evidence map›Paper›PMID 41439886›Full record

ReviewBiomimetics (Basel, Switzerland)2025

Artificial Intelligence in Thermal Ablation: Current Applications and Future Directions in Microwave Technologies.

Kealan Westby, Daniel Westby, Kevin McKevitt, Brian M Moloney

Abstract readReview
In one paragraph

Review in Biomimetics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kealan WestbyBlackrock Health Galway Clinic, H91 HHT0 Galway, Ireland.
Daniel WestbyDepartment of Radiology, University Hospital Limerick, St Nessans Rd, Dooradoyle, V94 F858 Limerick, Ireland.ORCID 0000-0001-8639-1169
Kevin McKevittDepartment of Radiology, University Hospital Limerick, St Nessans Rd, Dooradoyle, V94 F858 Limerick, Ireland.
Brian M MoloneyDepartment of Radiology, University Hospital Limerick, St Nessans Rd, Dooradoyle, V94 F858 Limerick, Ireland.ORCID 0000-0003-3179-6657

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly shaping interventional oncology, with growing interest in its application across thermal ablation modalities such as radiofrequency ablation (RFA), cryoablation, high-intensity focused ultrasound (HIFU), and microwave ablation (MWA). This review characterises the current landscape of AI-enhanced thermal ablation, with particular emphasis on emerging opportunities within MWA technologies. We examine how AI-driven methods-convolutional neural networks, radiomics, and reinforcement learning-are being applied to optimise patient selection, automate image segmentation, predict treatment response, and support real-time procedural guidance. Comparative insights are provided across ablation modalities to contextualise the unique challenges and opportunities presented by microwave systems. Emphasis is placed on integrating AI into clinical workflows, ensuring safety, improving consistency, and advancing personalised therapy. Tables summarising AI methods and applications, a conceptual workflow figure, and a research gap analysis for MWA are included to guide future work. While existing applications remain largely investigational, the convergence of AI with advanced imaging and energy delivery holds significant promise for precision oncology. We conclude with a roadmap for research and clinical translation, highlighting the need for prospective validation, regulatory clarity, and interdisciplinary collaboration to support the adoption of AI-enabled thermal ablation into routine practice.

Indexed as

artificial intelligenceclinical translationimage-guided therapyinterventional oncologymachine learningmicrowave ablationradiomicssegmentationthermal ablationtreatment planning

Identifiers

PMID41439886
PMCPMC12730249

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.