Towards real time AI-augmented fluorescence-guided surgery: Evidence and translational readiness across neurosurgical, gynaecological, and thoracic oncology

Fluorescence-guided surgery (FgS) is increasingly used across oncologic specialties to enhance intraoperative visualisation of tumour tissue and lymphatic drainage; however, its clinical impact remains limited by heterogeneous tracer uptake, variable signal intensity, and reliance on subjective visual interpretation, leading to inter-operator variability, uncertainty at tumour margins, residual disease, and inconsistent nodal assessment. This narrative review examines the role of artificial intelligence (AI) in addressing these limitations, synthesising evidence published between January 2000 and December 2025 across neuro-oncology, gynaecological oncology, and thoracic oncology. In neuro-oncology, early clinical and preclinical studies have directly evaluated real-time AI-enhanced interpretation of intraoperative fluorescence, including quantitative analysis of 5-aminolevulinic acid (5-ALA) and hyperspectral imaging, providing proof-of-concept evidence that AI can augment margin detection beyond subjective visual assessment. In contrast, gynaecological and thoracic oncology currently lack validated studies in which AI directly interprets intraoperative fluorescence signals, despite fluorescence imaging being clinically established in both fields; instead, AI development in these specialties has progressed primarily in adjacent domains such as radiomics, digital pathology, risk stratification, surgical planning, and intraoperative computer vision, demonstrating technical maturity but limited integration into fluorescence-guided decision-making. Overall, the available evidence supports proof-of-concept feasibility for real-time AI-enhanced fluorescence interpretation in neuro-oncology, while identifying a clear translational gap in gynaecological and thoracic oncology that warrants targeted research to integrate existing AI capabilities into intraoperative fluorescence-guided surgery.

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Publication Details

Journal
Surgical Oncology
Published
2026-02-13
DOI
https://doi.org/10.1016/j.suronc.2026.102364
Primary Topic
Nanoplatforms for cancer theranostics
Type
article
Field-Weighted Citation Impact
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article

Towards real time AI-augmented fluorescence-guided surgery: Evidence and translational readiness across neurosurgical, gynaecological, and thoracic oncology

Un Hou Chan, Asif Hasan, Darcy M. Gabriel, Mirkomol Mirzarakhimov et al.
Surgical Oncology
Nanoplatforms for cancer theranostics
article

Towards real time AI-augmented fluorescence-guided surgery: Evidence and translational readiness across neurosurgical, gynaecological, and thoracic oncology

Un Hou Chan, Asif Hasan, Darcy M. Gabriel, Mirkomol Mirzarakhimov, Omar Shafi, Siena Martin, Saurabh Phadnis, Mauro Camacho
article en

Abstract

Fluorescence-guided surgery (FgS) is increasingly used across oncologic specialties to enhance intraoperative visualisation of tumour tissue and lymphatic drainage; however, its clinical impact remains limited by heterogeneous tracer uptake, variable signal intensity, and reliance on subjective visual interpretation, leading to inter-operator variability, uncertainty at tumour margins, residual disease, and inconsistent nodal assessment. This narrative review examines the role of artificial intelligence (AI) in addressing these limitations, synthesising evidence published between January 2000 and December 2025 across neuro-oncology, gynaecological oncology, and thoracic oncology. In neuro-oncology, early clinical and preclinical studies have directly evaluated real-time AI-enhanced interpretation of intraoperative fluorescence, including quantitative analysis of 5-aminolevulinic acid (5-ALA) and hyperspectral imaging, providing proof-of-concept evidence that AI can augment margin detection beyond subjective visual assessment. In contrast, gynaecological and thoracic oncology currently lack validated studies in which AI directly interprets intraoperative fluorescence signals, despite fluorescence imaging being clinically established in both fields; instead, AI development in these specialties has progressed primarily in adjacent domains such as radiomics, digital pathology, risk stratification, surgical planning, and intraoperative computer vision, demonstrating technical maturity but limited integration into fluorescence-guided decision-making. Overall, the available evidence supports proof-of-concept feasibility for real-time AI-enhanced fluorescence interpretation in neuro-oncology, while identifying a clear translational gap in gynaecological and thoracic oncology that warrants targeted research to integrate existing AI capabilities into intraoperative fluorescence-guided surgery.

Surgical OncologyVol. 65
St George's Hospital (GB), St Thomas' Hospital (GB), Royal London Hospital (GB), Brunel University of London (GB)
Peace, Justice and strong institutions
Openalex Percentile: Top 59%
Nanoplatforms for cancer theranostics
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