{"id":3574,"date":"2025-05-08T13:57:00","date_gmt":"2025-05-08T03:57:00","guid":{"rendered":"https:\/\/harrison.ai\/?p=3574"},"modified":"2025-10-07T05:32:58","modified_gmt":"2025-10-06T18:32:58","slug":"from-challenge-to-solution-how-annalise-ai-is-enhancing-radiology-at-santandrea-hospital-italy","status":"publish","type":"post","link":"https:\/\/harrison.ai\/from-challenge-to-solution-how-annalise-ai-is-enhancing-radiology-at-santandrea-hospital-italy\/","title":{"rendered":"From Challenge to Solution: How Harrison.ai is Enhancing Radiology at Sant\u2019Andrea Hospital, Italy"},"content":{"rendered":"
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As a referral centre for thoracic cancer, Sant\u2019Andrea University Hospital in Rome, Italy, manages a significant imaging workload \u2013 interpreting over 20,000 chest X-rays every year. With growing demand and limited time, the need for intelligent support became clear.<\/p>\n

Addressing the need for AI in radiology<\/strong><\/p>\n

At Harrison.ai, we\u2019re focused on providing solutions that tackle specific challenges. We spoke with Professor Andrea Laghi, Chairman of Radiology at Sant\u2019Andrea, to hear his early impressions of our comprehensive and customisable AI solution. Here\u2019s what he had to say about how Harrison.ai solutions are already making a difference, highlighting the need for AI in healthcare to assist with routine workflows and improve efficiency.<\/p>\n

“We have a large demand for chest X-rays\u2026 we need a solution [for] helping our radiologists in reducing the workload.”<\/em><\/p>\n

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Professor Andrea Laghi explains why he chose Harrison.ai.<\/p>\n

Why Harrison.ai?<\/h3>\n

Despite being a referral centre for thoracic cancer, when selecting the right AI solution, Professor Laghi and his team were focused on finding a decision-support AI solution that identified more than a single or limited set of findings. \u00a0They were seeking a comprehensive solution capable of supporting a broad spectrum of diagnostic needs.<\/p>\n

\u201cI didn\u2019t want a software making a simple, single task\u2026 I want a more comprehensive software giving us an overall evaluation of the different findings in a chest X-ray.\u201d<\/em><\/p>\n

Harrison.ai Chest X-ray\u2019s ability to identify up to 124 different findings, including trauma, incidental findings and technical factors, made it the ideal choice for Sant\u2019Andrea Hospital. “The CXR solution is exactly what we were looking for\u2026 reporting more than a hundred different findings.”<\/p>\n \n <\/div>\n <\/div>\n <\/section>\n\n\n\n

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“The CXR solution is exactly what we were looking for\u2026 reporting more than a hundred different findings.”<\/q><\/blockquote> <\/div>\n <\/section>\n\n\n\n
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Early impact and positive feedback<\/h3>\n

Within the first few weeks of deployment, the AI tool had already received positive feedback from clinical users, particularly for its ability to aid in the interpretation of complex post-operative chest X-rays \u2013 often some of the most challenging to interpret. These scans must distinguish between normal post-surgical changes, potential complications, and incidental findings while balancing their limited diagnostic value against associated risks. Proper interpretation demands both clinical context and expertise. As Professor Laghi shared:<\/p>\n \n <\/div>\n <\/div>\n <\/section>\n\n\n\n

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“Our most important positive feedback at the moment is in the post-operative chest X-ray, which are often quite difficult to interpret\u2026 Our radiologists now like the AI tool, especially in this setting.”<\/q><\/blockquote> <\/div>\n <\/section>\n\n\n\n
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The future of AI in radiology and healthcare<\/h3>\n

Looking ahead, Professor Laghi is confident that AI in healthcare diagnostics will continue to evolve, becoming an essential tool for radiologists facing increasing demand and limited resources.<\/p>\n

\u201cAI is a part of the healthcare and in particular of the radiological world\u2026 We need to maximise efficiency in our work while preserving quality. We need support from outside.\u201d<\/em><\/p>\n

At Harrison.ai, we\u2019re proud to be part of this transformation, offering scalable AI solutions that enhance medical imaging analysis and improve healthcare delivery worldwide.<\/p>\n

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Disclaimer: Harrison.ai chest X-ray (CXR) was previously marketed as Annalise Enterprise and Annalise Container.<\/p>\n <\/div>\n \n <\/div>\n <\/div>\n <\/section>\n\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[28],"tags":[],"region":[35],"product":[],"class_list":["post-3574","post","type-post","status-publish","format-standard","hentry","category-case-studies","region-emea"],"acf":[],"_links":{"self":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts\/3574","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\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/comments?post=3574"}],"version-history":[{"count":4,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts\/3574\/revisions"}],"predecessor-version":[{"id":5202,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts\/3574\/revisions\/5202"}],"wp:attachment":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/media?parent=3574"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/categories?post=3574"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/tags?post=3574"},{"taxonomy":"region","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/region?post=3574"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/product?post=3574"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}