{"id":6814,"date":"2026-04-17T13:49:14","date_gmt":"2026-04-17T13:49:14","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/6814\/"},"modified":"2026-04-17T13:49:14","modified_gmt":"2026-04-17T13:49:14","slug":"with-ramaai-thailands-radiologists-get-ai-assistance-to-screen-x-rays","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/6814\/","title":{"rendered":"With RAMAAI, Thailand\u2019s radiologists get AI assistance to screen X-rays"},"content":{"rendered":"

Read this article in Thai<\/a><\/p>\n

Thailand has about 2,000 radiologists, who between them screen over 30 million X-rays produced by the country\u2019s healthcare system annually. That means on average, a radiologist assesses 15,000 images a year, a heavy workload these medical professionals say can affect their performance.<\/p>\n

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\u201cA single radiologist often reviews 60 to 200 chest X-rays daily, spending between 5 and 15 minutes on each. That number can even exceed 300 in busier hospitals,\u201d said Dr.\u00a0Narissara\u00a0Chobarun, radiologist in the thoracic radiology unit at the Faculty of Medicine\u00a0Ramathibodi\u00a0Hospital,\u00a0Mahidol University. \u201cThis can lead to fatigue, which could affect the speed and accuracy of their diagnosis, particularly for subtle or early-stage lesions that are often difficult to detect.\u201d<\/p>\n

To address this issue, Ramathibodi\u00a0Hospital developed the RAMAAI CXR Solution, an AI assistant running on Microsoft Azure\u2019s cloud platform that screens chest X-rays and helps doctors prioritize severe or infectious conditions.<\/p>\n

RAMAAI can detect up to\u202f16 types of diseases and abnormalities, including lung nodules, pneumonia and COPD, or chronic obstructive pulmonary disease.<\/p>\n

It also has a dedicated module for diagnosing tuberculosis, a lung infection commonly known as TB. RAMAAI not only detects TB, it can predict from the X-ray image whether the patient is at the infectious stage \u2013 a critical capability for controlling disease spread in high-incidence countries like Thailand. \u201cIt plays a vital role in initial screening.\u00a0If an image shows a high probability of abnormality, the system flags it for immediate review, ensuring patients receive\u00a0timely\u00a0diagnosis and care,\u201d said Dr.\u00a0Narissara. \u201cAdditionally, the system employs a\u202fheatmap\u202fto highlight suspicious areas, enhancing the thoroughness of chest X-ray\u00a0interpretations.\u201d<\/p>\n

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The AI was developed in-house and trained on data from chest X-rays of patients at\u00a0Ramathibodi\u00a0Hospital. This gives it a deep understanding of Thai people\u2019s physiology and disease patterns, for more accurate and reliable diagnoses of the local population.\u00a0<\/p>\n

Dr. Chayanin Nitivattanakul, Assistant Dean for Innovation and Partnership and Attending Radiologist in the Thoracic Radiology Unit at the Faculty of Medicine Ramathibodi Hospital, Mahidol University said, \u201cWhen it was tested during development on historical X-rays where lung cancer diagnoses were initially missed, RAMAAI demonstrated a\u202f72% increase in lesion identification.\u201d<\/p>\n

Improving healthcare outcomes\u00a0<\/p>\n

RAMAAI was rolled out in 8 public hospitals nationwide including Bangkok, Samut Prakarn, Chonburi, Lampang and Chaing Rai serving about 2,000 medical professionals.<\/p>\n

So far, it has processed over 1,500-2,000 medical images daily, with more than 500,000 images analyzed to date. According to Dr. Chayanin, using RAMAAI has boosted doctors\u2019 overall lesion detection accuracy by over 20%<\/p>\n

Scaling impact for equitable healthcare\u00a0<\/p>\n

Ramathibodi\u00a0Hospital now hopes to expand the use of RAMAAI to more hospitals and further improve its technology.<\/p>\n

Another 10 public hospitals are expected to adopt it by June this year. Meanwhile, Thailand\u2019s Department of Medical Services\u00a0plans to pilot the AI assistant across its affiliated hospitals.<\/p>\n

This expansion prioritizes remote areas, ensuring\u00a0equitable\u00a0access to quality diagnostics. RAMAAI is also engineered for integration with\u202fmobile X-ray units, which are used in rural regions with fewer medical facilities.<\/p>\n

Having\u202fMicrosoft Azure\u202fas its cloud platform supports these future plans. \u201cWe chose Microsoft Azure because Microsoft is a global leader, offering internationally standardized technology and innovation, paramount data security, and flexible scalability to support future expansion to numerous hospitals and users,\u201d said Dr.\u00a0Chayanin.\u00a0<\/p>\n

The hospital is also experimenting with AI models available in Microsoft Foundry, including BiomedCLIP, a medical imaging model, and the Phi\u20113\u2011Mini compact language model. These are being integrated into its CXRReportGen, a pilot multimodal AI tool that can help draft written reports after the X-rays have been analyzed. \u201cRAMAAI helps identify abnormalities more effectively,\u201d said Dr.\u00a0Chayanin. \u201cEarlier intervention for patients leads to\u00a0timely\u00a0care and reduced complications, infections, and disease spread.\u00a0Ultimately, this\u00a0lowers overall public healthcare costs.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"Read this article in Thai Thailand has about 2,000 radiologists, who between them screen over 30 million X-rays…\n","protected":false},"author":2,"featured_media":6815,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,201,6216,1657,6217],"class_list":{"0":"post-6814","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-cloud","11":"tag-community-partnerships","12":"tag-health","13":"tag-inclusion"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/6814","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=6814"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/6814\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/6815"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=6814"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=6814"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=6814"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}