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	<pub:InProceedings rdf:about="http://ebiquity.umbc.edu/paper/html/id/1207/LLM-based-Knowledge-Graph-Approach-to-Automating-Medical-Device-Regulatory-Compliance">
		<rdfs:label><![CDATA[LLM based Knowledge Graph Approach to Automating Medical Device Regulatory Compliance]]></rdfs:label>
		<pub:title><![CDATA[LLM based Knowledge Graph Approach to Automating Medical Device Regulatory Compliance]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2025-12-11T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[Advanced medical devices increasingly rely on AI driven frameworks to automate compliance processes, ensuring safety and efficacy while reducing regulatory burdens. In the US, software-based medical devices, including those utilizing AI/ML models, are regulated by the FDA’s Center for Devices and Radiological Health (CDRH) under the Code of Federal Regulations (CFR) Title 21. These regulations are extensive, cross-referenced documents that require significant human effort to parse, leading to high compliance costs for manufacturers. We propose a novel, semantically rich framework that extracts regulatory knowledge from FDA documents and translates it into a machine-processable format. Our system encodes regulatory knowledge into an OWL/RDF based knowledge graph and uses the Mistral 7B Instruct model to dynamically generate SPARQL queries, perform compliance reasoning, and produce structured
reports. This enables automated device classification (Class I, II, or III) and real time regulatory evaluation. Validated through
real-world use cases, our framework significantly reduces manual review effort, enhances interpretability, and accelerates time-to-market. The proposed approach integrates AI reasoning and semantic technologies to achieve scalable, transparent, and automated
regulatory compliance.]]></pub:abstract>
		<pub:organization><![CDATA[IEEE]]></pub:organization>
		<pub:counter>3502</pub:counter>
		<pub:tag><![CDATA[semantic web; knowledge graph; large language model; medical device; fda; code of federal regulations]]></pub:tag>
		<pub:booktitle><![CDATA[2025 IEEE International Conference on Big Data (BigData)]]></pub:booktitle>
		<pub:publisher><![CDATA[IEEE]]></pub:publisher>
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					<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Karuna/Joshi"><person:name><![CDATA[Karuna Pande Joshi]]></person:name><rdfs:label><![CDATA[Karuna Pande Joshi]]></rdfs:label></person:Person>
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							<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Subhankar/Chattoraj"><person:name><![CDATA[Subhankar Chattoraj]]></person:name><rdfs:label><![CDATA[Subhankar Chattoraj]]></rdfs:label></person:Person>
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<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Karuna/Joshi"><person:name><![CDATA[Karuna Pande Joshi]]></person:name><rdfs:label><![CDATA[Karuna Pande Joshi]]></rdfs:label></person:Person>
		</pub:firstAuthor>
		<pub:relatedProject><project:PastProject rdf:about="http://ebiquity.umbc.edu/project/html/id/105/ALDA-Automated-Legal-Document-Analytics"><project:title><![CDATA[ALDA: Automated Legal Document Analytics]]></project:title><rdfs:label><![CDATA[ALDA: Automated Legal Document Analytics]]></rdfs:label></project:PastProject></pub:relatedProject>
		<pub:relatedProject><project:ActiveProject rdf:about="http://ebiquity.umbc.edu/project/html/id/111/Medical-Device-Regulatory-Compliance"><project:title><![CDATA[Medical Device Regulatory Compliance]]></project:title><rdfs:label><![CDATA[Medical Device Regulatory Compliance]]></rdfs:label></project:ActiveProject></pub:relatedProject>
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			<pub:softCopyFormat><![CDATA[PDF Document]]></pub:softCopyFormat>
			<pub:softCopyURI><![CDATA[http://ebiquity.umbc.edu/get/a/publication/1476.pdf]]></pub:softCopyURI>
			<pub:softCopySize>1288128</pub:softCopySize>
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