{"id":4568,"date":"2025-10-01T18:20:36","date_gmt":"2025-10-01T08:20:36","guid":{"rendered":"https:\/\/harrison.ai\/?p=4568"},"modified":"2025-10-07T08:27:07","modified_gmt":"2025-10-06T21:27:07","slug":"a-second-set-of-eyes-helping-radiology-experts-at-bradford-teaching-hospitals","status":"publish","type":"post","link":"https:\/\/harrison.ai\/a-second-set-of-eyes-helping-radiology-experts-at-bradford-teaching-hospitals\/","title":{"rendered":"The \u2018second set of eyes\u2019 helping radiology experts at Bradford Teaching Hospitals"},"content":{"rendered":"\n\n\n\n
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Bradford Teaching Hospitals provides inpatient and outpatient services for approximately 550,000 people living in Bradford and communities across Yorkshire, and specialist services for about 1.1 million people. Its staff of 6,500+ work across Bradford Royal Infirmary, St Luke\u2019s Hospital, and five community sites.<\/p>\n

With its vision to be an \u201coutstanding provider of healthcare, research and education\u201d, the Trust aims to be recognised for innovating and delivering exceptional patient care.<\/p>\n

The Trust\u2019s radiology services form part of the Yorkshire Imaging Collaborative (YIC), a collection of six NHS radiology departments that work together across West Yorkshire and Harrogate.<\/p>\n<\/div> <\/div>\n <\/div>\n <\/section>\n\n\n\n

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Diagnosing lung cancer earlier for better patient outcomes<\/h2>\n

Respiratory conditions are a leading cause of illness and premature death in the Bradford district, and lung cancer is among the key drivers of mortality\u2014with 500 people on average dying from respiratory-related diseases in the district each year. 1<\/sup><\/p>\n

Chest x-rays play a crucial role in the diagnostic work-up for respiratory issues. Reflecting its commitment to innovation in patient care BTHFT (as part of YIC) successfully bid to procure an AI Diagnostic Fund (AIDF) grant for an AI tool that could help accelerate detection of lung issues.<\/p>\n

It specifically wanted a solution that could support staff, enhance accuracy and prioritise patients with pathology, thereby improving patient care.<\/p>\n

Dr Mark Kon, Clinical Radiology Lead at BTHFT explains it\u2019s rare for reporting staff to overlook anything on a chest x-ray, but Harrison.ai’s tool acts like a \u201csecond pair of eyes\u201d, complementing their expertise.<\/p>\n

\u201cThe AI can highlight things that it has seen and prompt the reporter to [take] a second look to make sure we\u2019re not missing things,\u201d he says.<\/p>\n

\u201cWe were one of the first groups to take on Harrison.ai Chest X-ray first as a trial, and now as an established platform for reporting X-rays,\u201d says Dr Kon.<\/p>\n

Anna Jowett, Advanced Radiographic Practitioner at BTHFT, says reporting staff typically look at and report on the x-ray themselves first. \u201cAnd then we look at the AI tool and make sure that everything we\u2019ve reported is the same as the AI tool.\u201d<\/p>\n<\/div> <\/div>\n <\/div>\n <\/section>\n\n\n\n

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Supporting clinical decision-making for high-priority cases<\/h2>\n

The solution is helping them reach their goal of detecting abnormal cases faster, reading chest x-rays within minutes of them being taken.<\/p>\n

\u201cAI is available as soon as the x-ray is taken, so clinicians can see that image alongside the chest x-ray when they\u2019re in the clinical setting. It can be a real-time decision-making tool,\u201d says Ms Jowett. \u201cThe main benefit is that the AI can prioritise cases; so, cases where there are urgent findings can be prioritised to the top of the list.\u201d<\/p>\n

Dr Kon explains \u201cthis means patients can see a chest physician earlier, can get their CT scans earlier and can be discussed at lung cancer meetings earlier. The overall result is a quicker time to surgery or treatment.\u201d<\/p>\n<\/div> <\/div>\n

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Facilitating accuracy and supporting staff in high-pressure situations<\/h2>

Preliminary studies by BTHFT back up staff observations. In a retrospective study[2] with 300 real-life cases of chest radiographs (including normal and abnormal cases), each case was analysed using the Harrison.ai CXR algorithm. The results were then validated against subsequent CT scans and an independent reference standard review by two experienced consultant radiologists, blinded to the AI outputs.<\/p>\n

The results showed that the solution correctly identified all 19 cases of multiple lung lesions (e.g. multiple lung metastases, miliary TB, other multi-focal infection), meaning it did not miss pathologies which have more than one lesion. Clinically, this suggests strong potential for helping detect cancer or early-onset infections. <\/p>\n

For single lung lesions, the AI correctly identified 81.5% of cases where a lesion was present and accurately confirmed the absence of a lesion in 98.8% of the scans where no lesion was found in a total of 54 cases. It also accurately detected all cases of pneumothorax, indicating it could be a reliable tool in emergency or triage settings where quick and accurate decisions are critical.<\/p>\n

Ms Jowett notes having access to Harrison.ai CXR is especially helpful under stressful circumstances. <\/p>\n

\u201cAll our practitioners have to have a high accuracy level anyway,\u201d she explains. \u201cBut where a human can suffer from fatigue or interruptions, the AI tool is a constant. So, pairing a human and the AI together can provide better accuracy.\u201d <\/p>\n<\/div>

Dr Kon agrees: \u201cNot only is it useful for day-to-day reporting, but overnight in the on-call situation, acute radiographers who have just taken a chest x-ray or the junior doctors on the ward can get AI to help them diagnose important things, and they can act on those chest x-rays in the middle of the night without having to wait for a formal report.\u201d<\/p>\n<\/div> <\/div>\n <\/div>\n <\/section>\n\n\n\n

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AI integral to radiology\u2019s future<\/h2>\n

Dr Kon thinks AI will be increasingly integrated into imaging. \u201cAI will help make faster and more accurate diagnoses,\u201d he says. \u201cI\u2019m sure in the long term this will help improve survival rates.\u201d<\/p>\n

Since deploying Harrison.ai CXR, BTHFT has also started using Harrison.ai CT Brain \u2013 an AI solution that can detect up to 130 findings on non-contrast head CT studies.<\/p>\n<\/div> <\/div>\n <\/div>\n <\/section>\n\n\n\n

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Ready to discuss how AI could drive clinical and financial outcomes for your organisation? <\/h2>\n <\/div>\n
\n Book a demo <\/span><\/a>\n <\/div>\n \n <\/div>\n <\/div>\n <\/section>\n\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":583,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[28],"tags":[],"region":[36],"product":[40],"class_list":["post-4568","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-studies","region-uk","product-cxr"],"acf":[],"_links":{"self":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts\/4568","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/comments?post=4568"}],"version-history":[{"count":5,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts\/4568\/revisions"}],"predecessor-version":[{"id":5160,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts\/4568\/revisions\/5160"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/media\/583"}],"wp:attachment":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/media?parent=4568"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/categories?post=4568"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/tags?post=4568"},{"taxonomy":"region","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/region?post=4568"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/product?post=4568"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}