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	<title>radiology Archives - HIT Leaders and News</title>
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		<title>Radiology Leaders Push for Structured AI Competency</title>
		<link>https://us.hitleaders.news/imaging/49756/radiology-leaders-push-for-structured-ai-competency/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=radiology-leaders-push-for-structured-ai-competency</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Mon, 13 Oct 2025 11:23:48 +0000</pubDate>
				<category><![CDATA[Imaging]]></category>
		<category><![CDATA[AAPM]]></category>
		<category><![CDATA[ACR]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[radiology]]></category>
		<category><![CDATA[RSNA]]></category>
		<category><![CDATA[SIIM]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=49756</guid>

					<description><![CDATA[<p>The accelerating integration of artificial intelligence in medical imaging is a present-state imperative. As radiology departments increasingly adopt AI tools to support diagnostics, workflow optimization, and quality assurance, the conversation has shifted from if to how. Yet even as algorithm performance improves, a critical challenge persists: how to equip the radiology workforce with the knowledge required to safely evaluate, implement, and oversee these tools across diverse clinical settings.</p>
<p>The post <a href="https://us.hitleaders.news/imaging/49756/radiology-leaders-push-for-structured-ai-competency/">Radiology Leaders Push for Structured AI Competency</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
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			</item>
		<item>
		<title>Bayer’s AI Retreat Exposes Fault Lines in Radiology Platform Strategy</title>
		<link>https://us.hitleaders.news/imaging/49693/bayers-ai-retreat-exposes-fault-lines-in-radiology-platform-strategy/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=bayers-ai-retreat-exposes-fault-lines-in-radiology-platform-strategy</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Mon, 15 Sep 2025 12:03:54 +0000</pubDate>
				<category><![CDATA[Imaging]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Bayer]]></category>
		<category><![CDATA[GE Healthcare]]></category>
		<category><![CDATA[radiology]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=49693</guid>

					<description><![CDATA[<p>Bayer has confirmed it will discontinue both its Calantic Digital Solutions platform and the services of its subsidiary, Blackford Analysis, marking a full retreat from the radiology AI platform business. The announcement effectively closes a five-year push to centralize access to artificial intelligence tools for diagnostic imaging, an initiative that, despite technical promise, struggled to secure meaningful traction in hospital workflows.</p>
<p>The post <a href="https://us.hitleaders.news/imaging/49693/bayers-ai-retreat-exposes-fault-lines-in-radiology-platform-strategy/">Bayer’s AI Retreat Exposes Fault Lines in Radiology Platform Strategy</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
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		<item>
		<title>Separating Human and AI Duties in Radiology</title>
		<link>https://us.hitleaders.news/core-categories/ai-machine-learning/49380/separating-human-and-ai-duties-in-radiology/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=separating-human-and-ai-duties-in-radiology</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Tue, 05 Aug 2025 11:03:44 +0000</pubDate>
				<category><![CDATA[AI/Machine Learning]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[certification]]></category>
		<category><![CDATA[HIMSS]]></category>
		<category><![CDATA[radiology]]></category>
		<category><![CDATA[RSNA]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=49380</guid>

					<description><![CDATA[<p>Artificial intelligence entered diagnostic imaging with predictions of either superseding radiologists or amplifying their productivity. A Radiology editorial by Pranav Rajpurkar and Eric Topol argues that neither extreme matches current reality. Field surveys from HIMSS show eight in ten U.S. health systems have piloted at least one imaging algorithm, yet most frontline readers remain uncertain when to rely on machine guidance.</p>
<p>The post <a href="https://us.hitleaders.news/core-categories/ai-machine-learning/49380/separating-human-and-ai-duties-in-radiology/">Separating Human and AI Duties in Radiology</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
]]></description>
		
		
		
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		<item>
		<title>Radiologists Share Tips to Prevent AI Bias</title>
		<link>https://us.hitleaders.news/imaging/48566/radiologists-share-tips-to-prevent-ai-bias/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=radiologists-share-tips-to-prevent-ai-bias</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Thu, 29 May 2025 11:50:37 +0000</pubDate>
				<category><![CDATA[Imaging]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[AI Bias]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[Radiological Society of North America]]></category>
		<category><![CDATA[radiology]]></category>
		<category><![CDATA[RSNA]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=48566</guid>

					<description><![CDATA[<p>"AI has the potential to revolutionize radiology by improving diagnostic accuracy and access to care," said lead author Paul H. Yi, M.D., associate member (associate professor) in the Department of Radiology and director of Intelligent Imaging Informatics at St. Jude Children's Research Hospital in Memphis, Tennessee. "However, AI algorithms can sometimes exhibit biases, unintentionally disadvantaging certain groups based on age, sex or race."</p>
<p>The post <a href="https://us.hitleaders.news/imaging/48566/radiologists-share-tips-to-prevent-ai-bias/">Radiologists Share Tips to Prevent AI Bias</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
]]></description>
		
		
		
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		<title>Fine-tuned LLMs Boost Error Detection in Radiology Reports</title>
		<link>https://us.hitleaders.news/imaging/48564/fine-tuned-llms-boost-error-detection-in-radiology-reports/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=fine-tuned-llms-boost-error-detection-in-radiology-reports</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Wed, 28 May 2025 13:38:53 +0000</pubDate>
				<category><![CDATA[Imaging]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[Radiological Society of North America]]></category>
		<category><![CDATA[radiology]]></category>
		<category><![CDATA[RSNA]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=48564</guid>

					<description><![CDATA[<p>A type of artificial intelligence called fine-tuned large language models (LLMs) greatly enhances error detection in radiology reports, according to a new study published today in Radiology, a journal of the Radiological Society of North America (RSNA). Researchers said the findings point to an important role for this technology in medical proofreading.</p>
<p>The post <a href="https://us.hitleaders.news/imaging/48564/fine-tuned-llms-boost-error-detection-in-radiology-reports/">Fine-tuned LLMs Boost Error Detection in Radiology Reports</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
]]></description>
		
		
		
			</item>
		<item>
		<title>Special Report Highlights LLM Cybersecurity Threats in Radiology</title>
		<link>https://us.hitleaders.news/imaging/48433/special-report-highlights-llm-cybersecurity-threats-in-radiology/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=special-report-highlights-llm-cybersecurity-threats-in-radiology</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Mon, 19 May 2025 11:22:19 +0000</pubDate>
				<category><![CDATA[Imaging]]></category>
		<category><![CDATA[cybersecurity]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[Radiological Society of North America]]></category>
		<category><![CDATA[radiology]]></category>
		<category><![CDATA[RSNA]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=48433</guid>

					<description><![CDATA[<p>In a new special report, researchers address the cybersecurity challenges of large language models (LLMs) and the importance of implementing security measures to prevent LLMs from being used maliciously in the health care system. The special report was published today in Radiology: Artificial Intelligence, a journal of the Radiological Society of North America (RSNA).</p>
<p>The post <a href="https://us.hitleaders.news/imaging/48433/special-report-highlights-llm-cybersecurity-threats-in-radiology/">Special Report Highlights LLM Cybersecurity Threats in Radiology</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
]]></description>
		
		
		
			</item>
		<item>
		<title>Vision-based ChatGPT Shows Deficits Interpreting Radiologic Images</title>
		<link>https://us.hitleaders.news/imaging/46103/vision-based-chatgpt-shows-deficits-interpreting-radiologic-images/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=vision-based-chatgpt-shows-deficits-interpreting-radiologic-images</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Wed, 04 Sep 2024 11:40:33 +0000</pubDate>
				<category><![CDATA[Imaging]]></category>
		<category><![CDATA[ChatGPT]]></category>
		<category><![CDATA[radiology]]></category>
		<category><![CDATA[RSNA]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=46103</guid>

					<description><![CDATA[<p>Researchers evaluating the performance of ChatGPT-4 Vision found that the model performed well on text-based radiology exam questions but struggled to answer image-related questions accurately.</p>
<p>The post <a href="https://us.hitleaders.news/imaging/46103/vision-based-chatgpt-shows-deficits-interpreting-radiologic-images/">Vision-based ChatGPT Shows Deficits Interpreting Radiologic Images</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
]]></description>
		
		
		
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		<item>
		<title>GE HealthCare and University Hospitals Enter 10-Year Strategic Collaboration</title>
		<link>https://us.hitleaders.news/imaging/41345/ge-healthcare-and-university-hospitals-enter-10-year-strategic-collaboration/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=ge-healthcare-and-university-hospitals-enter-10-year-strategic-collaboration</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Mon, 09 Oct 2023 13:13:44 +0000</pubDate>
				<category><![CDATA[Imaging]]></category>
		<category><![CDATA[GE Healthcare]]></category>
		<category><![CDATA[radiology]]></category>
		<category><![CDATA[University Hospitals]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=41345</guid>

					<description><![CDATA[<p>Realizing a vision to deliver the best care to patients close to where they live in northern Ohio, GE HealthCare and University Hospitals (UH) announced a 10-year strategic collaboration to bring GE HealthCare technologies to UH across its radiology practice and UH Harrington Heart &#038; Vascular Institute, providing contemporary equipment and digital solutions to improve processes for the benefit of patients and clinicians.</p>
<p>The post <a href="https://us.hitleaders.news/imaging/41345/ge-healthcare-and-university-hospitals-enter-10-year-strategic-collaboration/">GE HealthCare and University Hospitals Enter 10-Year Strategic Collaboration</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
]]></description>
		
		
		
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		<item>
		<title>Interventional Radiology Gains Nearly $2 Million Investment</title>
		<link>https://us.hitleaders.news/imaging/40502/interventional-radiology-gains-nearly-2-million-investment/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=interventional-radiology-gains-nearly-2-million-investment</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Wed, 17 May 2023 13:30:03 +0000</pubDate>
				<category><![CDATA[Imaging]]></category>
		<category><![CDATA[Interventional Radiology]]></category>
		<category><![CDATA[radiology]]></category>
		<category><![CDATA[University Hospitals Parma Medical Center]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=40502</guid>

					<description><![CDATA[<p>Nearly $2 million was invested in an upgrade of Interventional Radiology equipment at University Hospitals Parma Medical Center, enabling the care team to provide more complex exams for a wider range of patients. UH Parma now has the most sophisticated IR lab in the entire UH system, which includes more than 20 hospitals, including five joint ventures.</p>
<p>The post <a href="https://us.hitleaders.news/imaging/40502/interventional-radiology-gains-nearly-2-million-investment/">Interventional Radiology Gains Nearly $2 Million Investment</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
]]></description>
		
		
		
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		<item>
		<title>UF Health Launches AI Research Initiative in Radiology</title>
		<link>https://us.hitleaders.news/academic-research/40424/uf-health-launches-ai-research-initiative-in-radiology/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=uf-health-launches-ai-research-initiative-in-radiology</link>
		
		<dc:creator><![CDATA[Jason Free]]></dc:creator>
		<pubDate>Wed, 03 May 2023 13:19:42 +0000</pubDate>
				<category><![CDATA[Academic Research]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Nuance Communications]]></category>
		<category><![CDATA[radiology]]></category>
		<category><![CDATA[University of Florida]]></category>
		<category><![CDATA[University of Florida Health]]></category>
		<guid isPermaLink="false">https://us.hitleaders.news/?p=40424</guid>

					<description><![CDATA[<p>Using artificial intelligence tools to make radiologists’ work more precise and efficient is the goal as University of Florida Health researchers embark on an academic-industry collaboration.</p>
<p>The post <a href="https://us.hitleaders.news/academic-research/40424/uf-health-launches-ai-research-initiative-in-radiology/">UF Health Launches AI Research Initiative in Radiology</a> appeared first on <a href="https://us.hitleaders.news">HIT Leaders and News</a>.</p>
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