Difference between revisions of "Cognitive Computing"
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== Introduction == | == Introduction == | ||
| − | Cognitive computing is a method of using computerized models to simulate human thought process in complex situations where the answers might be ambiguous and uncertain | + | Cognitive computing is a method of using computerized models to simulate human thought process in complex situations where the answers might be ambiguous and uncertain [1]. This is similar to the grey area of human thought rather than concrete black and white process that apply to many workflows in clinical information systems. It can involve [[artificial intelligence]], machine learning and [[Natural language processing (NLP)]][2]. Cognitive computing systems are often based on artificial neural networks which are inspired by the human brain and are able to learn from data and improve their performance over time [3]. |
== Cognitive Computing in Healthcare == | == Cognitive Computing in Healthcare == | ||
| − | Systems using cognitive computing can aid in diagnosing diseases, developing new treatments and managing patient care. | + | Systems using cognitive computing can aid in diagnosing diseases, developing new treatments and managing patient care[4]. With cognitive computing, enormous health-related data can be harnessed including from personal fitness trackers, mobile apps, electronic medical records and genomic and clinical research[5]. IBM Watson defines this era of cognitive computing in healthcare as the era of cognitive health which bring together individual data with social and medical data from diverse sources including research and medical records. Cognitive systems can also help health information sharing for better patient outcomes. Cognitive computing can rapidly and intelligently parse through disparate data to help coordinate care. It is important to note that cognitive computing involves a category of analytics that are taught or learned rather than being programmed or rules-based analytics and computing. For example, in [[Epic]] a lot of analytics and computing is based on conditional rules with various logic clauses. On the other hand, there is now some cognitive computing in [[Epic]] that is used in risk scoring as well as in generative AI. For example, Microsoft OpenAI will be used for a generative AI run inbox messaging system that should save provider pajama time[6]. |
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| + | == Artificial Intelligence used in wearables and mobile apps == | ||
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== References == | == References == | ||
| − | 1. | + | 1. Coccoli, M., Maresca, P., & Stanganelli, L. (2017). The role of big data and cognitive computing in the learning process. J. Vis. Lang. Comput., 38, 97-103. http://dx.doi.org/10.1016/j.jvlc.2016.03.002 |
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| + | 2. Norden, A. D., Dankwa-Mullan, I., Urman, A., Suarez, F., & Rhee, K. (2018). Realizing the Promise of Cognitive Computing in Cancer Care: Ushering in a New Era. JCO clinical cancer informatics, 2, 1–6. https://doi.org/10.1200/CCI.17.00049 | ||
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| + | 3. Esteva A, Kuprel B, Novoa RA, et al: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115-118, 2017. https://doi.org/10.1038/nature21056 | ||
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| + | 4. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future healthcare journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095 | ||
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| + | 5. Ahmed, M.N., Toor, A.S., O'Neil, K., & Friedland, D. (2017). Cognitive Computing and the Future of Health Care Cognitive Computing and the Future of Healthcare: The Cognitive Power of IBM Watson Has the Potential to Transform Global Personalized Medicine. IEEE Pulse, 8, 4-9. | ||
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| + | 6. https://www.veronapress.com/news/epic-microsoft-collaboration-will-bring-ai-technology-to-healthcare/article_e6242b62-e91b-11ed-812f-7fedc9f9b265.html | ||
Revision as of 01:38, 30 April 2024
Submitted by Ravi Janumpally
Contents
Introduction
Cognitive computing is a method of using computerized models to simulate human thought process in complex situations where the answers might be ambiguous and uncertain [1]. This is similar to the grey area of human thought rather than concrete black and white process that apply to many workflows in clinical information systems. It can involve artificial intelligence, machine learning and Natural language processing (NLP)[2]. Cognitive computing systems are often based on artificial neural networks which are inspired by the human brain and are able to learn from data and improve their performance over time [3].
Cognitive Computing in Healthcare
Systems using cognitive computing can aid in diagnosing diseases, developing new treatments and managing patient care[4]. With cognitive computing, enormous health-related data can be harnessed including from personal fitness trackers, mobile apps, electronic medical records and genomic and clinical research[5]. IBM Watson defines this era of cognitive computing in healthcare as the era of cognitive health which bring together individual data with social and medical data from diverse sources including research and medical records. Cognitive systems can also help health information sharing for better patient outcomes. Cognitive computing can rapidly and intelligently parse through disparate data to help coordinate care. It is important to note that cognitive computing involves a category of analytics that are taught or learned rather than being programmed or rules-based analytics and computing. For example, in Epic a lot of analytics and computing is based on conditional rules with various logic clauses. On the other hand, there is now some cognitive computing in Epic that is used in risk scoring as well as in generative AI. For example, Microsoft OpenAI will be used for a generative AI run inbox messaging system that should save provider pajama time[6].
Artificial Intelligence used in wearables and mobile apps
References
1. Coccoli, M., Maresca, P., & Stanganelli, L. (2017). The role of big data and cognitive computing in the learning process. J. Vis. Lang. Comput., 38, 97-103. http://dx.doi.org/10.1016/j.jvlc.2016.03.002
2. Norden, A. D., Dankwa-Mullan, I., Urman, A., Suarez, F., & Rhee, K. (2018). Realizing the Promise of Cognitive Computing in Cancer Care: Ushering in a New Era. JCO clinical cancer informatics, 2, 1–6. https://doi.org/10.1200/CCI.17.00049
3. Esteva A, Kuprel B, Novoa RA, et al: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115-118, 2017. https://doi.org/10.1038/nature21056
4. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future healthcare journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095
5. Ahmed, M.N., Toor, A.S., O'Neil, K., & Friedland, D. (2017). Cognitive Computing and the Future of Health Care Cognitive Computing and the Future of Healthcare: The Cognitive Power of IBM Watson Has the Potential to Transform Global Personalized Medicine. IEEE Pulse, 8, 4-9.
