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Software that reads scans alongside radiologists, catching cancers earlier and predicting who is at risk.
Hundreds of FDA-cleared radiology AI devices exist; oncology use cases include mammography reading (Transpara, Lunit INSIGHT, MASAI trial in Sweden showed 29% more cancers detected with 44% less workload), lung nodule detection and malignancy scoring (Sybil, Optellum), prostate MRI, and risk models (Mirai). Foundation models linking images with text are emerging.
Deep convolutional and transformer networks trained on labelled imaging; increasingly self-supervised on large unlabelled corpora.
Nothing in the corpus depends on this yet.
Dependencies are what this technology cannot be delivered without: manufacturing steps, instruments, software, upstream methods. See its full chain on the map.
A Korean AI mammography reader used in screening programmes in Sweden and Australia and cleared in the US and Europe.
A London-built breast screening AI trialled across NHS sites as a second reader, now folded into RadNet's DeepHealth after a 2024 acquisition.
The first AI cleared to estimate how likely a lung nodule on a CT scan is to be cancer, helping doctors decide who needs a biopsy and who can wait.
The first AI cleared for 3D mammography in the US, now part of RadNet's imaging network.
The breast screening AI tested in Sweden's MASAI trial, where AI-supported reading found more cancers and nearly halved the radiologists' workload.
Screening programmes short of radiologists can read this as prospective evidence that a well-validated AI reader can take the second reader's seat without lowering cancer detection. It is a paired-reader study in one hospital, not a randomised trial, so MASAI and real-world follow-up carry the argument further.
AI can take over one reader's work in double-reading screening programmes while finding more cancers. Whether the extra cancers found are ones that would have harmed women, and whether interval cancers fall, is the question the trial's primary endpoint will answer.
Lung screening works when it uses volumetric nodule management, and it works against a no-screening control. The protocol underpins the UK Targeted Lung Health Check programme and European recommendations. Benefit in women remains less precisely estimated.
Query for this technology: (TITLE:"artificial intelligence" OR ABSTRACT:"artificial intelligence" OR TITLE:"deep learning" OR ABSTRACT:"deep learning") AND (TITLE:"mammography" OR ABSTRACT:"mammography" OR TITLE:"lung cancer screening" OR ABSTRACT:"lung cancer screening" OR TITLE:"radiology" OR ABSTRACT:"radiology") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about AI in radiology, not a curated reading list.
Shares A dedicated fund for randomised trials of cancer AI with patient outcomes, Continuous prospective validation for every oncology AI tool after deployment, Oncology workforce, Federated training of pathology and radiology models across hospitals.
Shares AI malignancy scores to end repeat scans and biopsies for benign lung nodules, Set each woman's mammogram interval from her last mammogram, using AI risk, Require stage-shift or interval-cancer endpoints for AI in cancer screening, Every routine CT scan checked by AI for early cancer signs, with a tracked follow-up pathway.
Shares Denise R. Aberle, Tumour volume doubling time, NHS Targeted Lung Health Check (lung cancer screening programme), NELSON: volume-based CT screening reduces lung cancer deaths with fewer false alarms.
Shares Radiogenomics: predicting radiation sensitivity from genes, Radiology imaging as a data modality (CT, MRI, TCIA), Tumour volume doubling time, Quantitative imaging biomarkers (RECIST, PERCIST, SUV, ADC).
Shares Require stage-shift or interval-cancer endpoints for AI in cancer screening, Molecular indolence classifiers bundled with every screening programme, Early detection, NHS Targeted Lung Health Check (lung cancer screening programme).
Shares Federated training of pathology and radiology models across hospitals, Federated learning and privacy-preserving AI, A federated learning consortium of cancer centres that jointly own the models, Pathology & radiology foundation models.
Shares Contrast-enhanced mammography, AI compute and model platforms for oncology, Breast MRI coils and abbreviated breast MRI, Dermoscopy, total-body photography & AI skin analysis.
Shares Contrast-enhanced mammography, CT, MRI and PET scanner manufacturing, Breast MRI coils and abbreviated breast MRI, Hand-held and point-of-care ultrasound.
Open-source projects that implement or serve this technology, from OnCo's own catalogue: licence and last activity as the repository reported them on the day of the fetch. Listing is not endorsement; check the licence before reuse and the validation before clinical use.
The PyTorch framework for deep learning in medical imaging, co-founded by NVIDIA and King's College London, used for tumour segmentation and detection research and products.
Commercial and regulated products that serve this technology. Each card says what is behind it: a regulator's database, the literature, a public body's list, or only the company's own words. Listing is not endorsement, and a clearance is a regulatory fact, not a clinical one.
Software that suppresses vessels on chest CT so nodules stand out, with a detection algorithm on top.
A mammography reading algorithm used in screening programmes, scoring each examination and marking suspicious findings for the reader.
Detection and density software for digital breast tomosynthesis and mammography, marking lesions and giving a case score.
A family of reading algorithms sold into screening and diagnostic imaging: mammography, tomosynthesis and chest radiographs, each cleared separately.
A family of chest radiograph and CT algorithms that flag lung nodules and other findings, deployed in screening and case-finding programmes in several countries.
A family of reading algorithms for CT and MR, including organ contouring for radiotherapy and prostate MR reading.
Open-source software, hardware and data projects catalogued by a third party, the Open Medical Registry, that bear on this technology. Listing is not endorsement; check each project's own licence and validation before clinical use.
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Meta-repository of screening mammography classifiers
From the Open Medical Registry (openmedical.sh), an MIT-licensed catalogue of open-source medicine. Blurbs are one line from each registry record; every project keeps its own licence.