@article{rosenberg_artificial_nodate,
	title = {Artificial {Swarm} {Intelligence} employed to {Amplify} {Diagnostic} {Accuracy} in {Radiology}},
	issn = {https://11s1ty2quyfy2qbmao3bwxzc-wpengine.netdna-ssl.com/wp-content/uploads/2018/09/ASI-for-Radiology-IEEE-IEMCON-2018.pdf},
	abstract = {Swarm Intelligence (SI) is a biological phenomenon in which groups of organisms amplify their combined intelligence by forming real-time systems. It has been studied for decades in fish schools, bird flocks, and bee swarms. Recent advances in networking and AI technologies have enabled distributed human groups to form closed-loop systems modeled after natural swarms. The process is referred to as Artificial Swarm Intelligence (ASI) and has been shown to significantly amplify group intelligence. The present research applies ASI technology to the field of medicine, exploring if small groups of networked radiologists can improve their diagnostic accuracy when reviewing chest X-rays for the presence of pneumonia by “thinking together” as an ASI system. Data was collected for individual diagnoses as well as for diagnoses made by the group working as a real-time ASI system. Diagnoses were also collected using a state-of-the-art deep learning system developed by Stanford University School of Medicine. Results showed that a small group of networked radiologists, when working as a real-time closed-loop ASI system, was significantly more accurate than the individuals on their own, reducing errors by 33\%, as well as significantly more accurate (22\%) than a stateof-the-art software-only solution using deep learning.},
	language = {en},
	journal = {IEMCON 2018 – 9th Annual Information Technology, Electronics, and Mobile Communication Conference},
	author = {Rosenberg, Louis and Willcox, Gregg and Ai, Unanimous and Halabi, Safwan and Lungren, Matthew},
	pages = {6},
}

{"_id":"JnXz5xMPuK22WAffg","bibbaseid":"rosenberg-willcox-ai-halabi-lungren-artificialswarmintelligenceemployedtoamplifydiagnosticaccuracyinradiology","author_short":["Rosenberg, L.","Willcox, G.","Ai, U.","Halabi, S.","Lungren, M."],"bibdata":{"bibtype":"article","type":"article","title":"Artificial Swarm Intelligence employed to Amplify Diagnostic Accuracy in Radiology","issn":"https://11s1ty2quyfy2qbmao3bwxzc-wpengine.netdna-ssl.com/wp-content/uploads/2018/09/ASI-for-Radiology-IEEE-IEMCON-2018.pdf","abstract":"Swarm Intelligence (SI) is a biological phenomenon in which groups of organisms amplify their combined intelligence by forming real-time systems. It has been studied for decades in fish schools, bird flocks, and bee swarms. Recent advances in networking and AI technologies have enabled distributed human groups to form closed-loop systems modeled after natural swarms. The process is referred to as Artificial Swarm Intelligence (ASI) and has been shown to significantly amplify group intelligence. The present research applies ASI technology to the field of medicine, exploring if small groups of networked radiologists can improve their diagnostic accuracy when reviewing chest X-rays for the presence of pneumonia by “thinking together” as an ASI system. Data was collected for individual diagnoses as well as for diagnoses made by the group working as a real-time ASI system. Diagnoses were also collected using a state-of-the-art deep learning system developed by Stanford University School of Medicine. Results showed that a small group of networked radiologists, when working as a real-time closed-loop ASI system, was significantly more accurate than the individuals on their own, reducing errors by 33%, as well as significantly more accurate (22%) than a stateof-the-art software-only solution using deep learning.","language":"en","journal":"IEMCON 2018 – 9th Annual Information Technology, Electronics, and Mobile Communication Conference","author":[{"propositions":[],"lastnames":["Rosenberg"],"firstnames":["Louis"],"suffixes":[]},{"propositions":[],"lastnames":["Willcox"],"firstnames":["Gregg"],"suffixes":[]},{"propositions":[],"lastnames":["Ai"],"firstnames":["Unanimous"],"suffixes":[]},{"propositions":[],"lastnames":["Halabi"],"firstnames":["Safwan"],"suffixes":[]},{"propositions":[],"lastnames":["Lungren"],"firstnames":["Matthew"],"suffixes":[]}],"pages":"6","bibtex":"@article{rosenberg_artificial_nodate,\n\ttitle = {Artificial {Swarm} {Intelligence} employed to {Amplify} {Diagnostic} {Accuracy} in {Radiology}},\n\tissn = {https://11s1ty2quyfy2qbmao3bwxzc-wpengine.netdna-ssl.com/wp-content/uploads/2018/09/ASI-for-Radiology-IEEE-IEMCON-2018.pdf},\n\tabstract = {Swarm Intelligence (SI) is a biological phenomenon in which groups of organisms amplify their combined intelligence by forming real-time systems. It has been studied for decades in fish schools, bird flocks, and bee swarms. Recent advances in networking and AI technologies have enabled distributed human groups to form closed-loop systems modeled after natural swarms. The process is referred to as Artificial Swarm Intelligence (ASI) and has been shown to significantly amplify group intelligence. The present research applies ASI technology to the field of medicine, exploring if small groups of networked radiologists can improve their diagnostic accuracy when reviewing chest X-rays for the presence of pneumonia by “thinking together” as an ASI system. Data was collected for individual diagnoses as well as for diagnoses made by the group working as a real-time ASI system. Diagnoses were also collected using a state-of-the-art deep learning system developed by Stanford University School of Medicine. Results showed that a small group of networked radiologists, when working as a real-time closed-loop ASI system, was significantly more accurate than the individuals on their own, reducing errors by 33\\%, as well as significantly more accurate (22\\%) than a stateof-the-art software-only solution using deep learning.},\n\tlanguage = {en},\n\tjournal = {IEMCON 2018 – 9th Annual Information Technology, Electronics, and Mobile Communication Conference},\n\tauthor = {Rosenberg, Louis and Willcox, Gregg and Ai, Unanimous and Halabi, Safwan and Lungren, Matthew},\n\tpages = {6},\n}\n\n","author_short":["Rosenberg, L.","Willcox, G.","Ai, U.","Halabi, S.","Lungren, M."],"key":"rosenberg_artificial_nodate","id":"rosenberg_artificial_nodate","bibbaseid":"rosenberg-willcox-ai-halabi-lungren-artificialswarmintelligenceemployedtoamplifydiagnosticaccuracyinradiology","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://api.zotero.org/groups/1019266/items?key=r9AeILtBKhutWeVj484T61Vv&format=bibtex&limit=100","dataSources":["BKfGCSmNGCp7WCs8R"],"keywords":[],"search_terms":["artificial","swarm","intelligence","employed","amplify","diagnostic","accuracy","radiology","rosenberg","willcox","ai","halabi","lungren"],"title":"Artificial Swarm Intelligence employed to Amplify Diagnostic Accuracy in Radiology","year":null}