The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Aaron Y. Lee - One of the best experts on this subject based on the ideXlab platform.
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Big data requirements for Artificial Intelligence.
Current opinion in ophthalmology, 2020Co-Authors: Sophia Y. Wang, Suzann Pershing, Aaron Y. LeeAbstract:Purpose of review To summarize how big data and Artificial Intelligence technologies have evolved, their current state, and next steps to enable future generations of Artificial Intelligence for ophthalmology. Recent findings Big data in health care is ever increasing in volume and variety, enabled by the widespread adoption of electronic health records (EHRs) and standards for health data information exchange, such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources. Simultaneously, the development of powerful cloud-based storage and computing architectures supports a fertile environment for big data and Artificial Intelligence in health care. The high volume and velocity of imaging and structured data in ophthalmology and is one of the reasons why ophthalmology is at the forefront of Artificial Intelligence research. Still needed are consensus labeling conventions for performing supervised learning on big data, promotion of data sharing and reuse, standards for sharing Artificial Intelligence model architectures, and access to Artificial Intelligence models through open application program interfaces (APIs). Summary Future requirements for big data and Artificial Intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of Artificial Intelligence by promoting standards for data labels, data sharing, Artificial Intelligence model architecture sharing, and accessible code and APIs.
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Big data requirements for Artificial Intelligence.
Current opinion in ophthalmology, 2020Co-Authors: Sophia Y. Wang, Suzann Pershing, Aaron Y. LeeAbstract:To summarize how big data and Artificial Intelligence technologies have evolved, their current state, and next steps to enable future generations of Artificial Intelligence for ophthalmology. Big data in health care is ever increasing in volume and variety, enabled by the widespread adoption of electronic health records (EHRs) and standards for health data information exchange, such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources. Simultaneously, the development of powerful cloud-based storage and computing architectures supports a fertile environment for big data and Artificial Intelligence in health care. The high volume and velocity of imaging and structured data in ophthalmology and is one of the reasons why ophthalmology is at the forefront of Artificial Intelligence research. Still needed are consensus labeling conventions for performing supervised learning on big data, promotion of data sharing and reuse, standards for sharing Artificial Intelligence model architectures, and access to Artificial Intelligence models through open application program interfaces (APIs). Future requirements for big data and Artificial Intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of Artificial Intelligence by promoting standards for data labels, data sharing, Artificial Intelligence model architecture sharing, and accessible code and APIs.
Michael V. Boland - One of the best experts on this subject based on the ideXlab platform.
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Artificial Intelligence in glaucoma
Current Opinion in Ophthalmology, 2019Co-Authors: Chengjie Zheng, Aakriti Garg, Thomas V Johnson, Michael V. BolandAbstract:Purpose of reviewThe use of computers has become increasingly relevant to medical decision-making, and Artificial Intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current Artificial Intelligence methods and their applications, to h
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Artificial Intelligence in glaucoma
Current opinion in ophthalmology, 2019Co-Authors: Chengjie Zheng, Aakriti Garg, Thomas V Johnson, Michael V. BolandAbstract:The use of computers has become increasingly relevant to medical decision-making, and Artificial Intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current Artificial Intelligence methods and their applications, to help the practicing ophthalmologist understand their potential impact on glaucoma care. Techniques used in Artificial Intelligence can successfully analyze and categorize data from visual fields, optic nerve structure [e.g., optical coherence tomography (OCT) and fundus photography], ocular biomechanical properties, and a combination thereof to identify disease severity, determine disease progression, and/or recommend referral for specialized care. Algorithms have become increasingly complex in recent years, utilizing both supervised and unsupervised methods of Artificial Intelligence. Impressive performance of these algorithms on previously unseen data has been reported, often outperforming standard global indices and expert observers. However, there remains no clearly defined gold standard for determining the presence and severity of glaucoma, which undermines the training of these algorithms. To improve upon existing methodologies, future work must employ more robust definitions of disease, optimize data inputs for Artificial Intelligence analysis, and improve methods of extracting knowledge from learned results. Artificial Intelligence has the potential to revolutionize the screening, diagnosis, and classification of glaucoma, both through the automated processing of large data sets, and by earlier detection of new disease patterns. In addition, Artificial Intelligence holds promise for fundamentally changing research aimed at understanding the development, progression, and treatment of glaucoma, by identifying novel risk factors and by evaluating the importance of existing ones.
Sophia Y. Wang - One of the best experts on this subject based on the ideXlab platform.
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Big data requirements for Artificial Intelligence.
Current opinion in ophthalmology, 2020Co-Authors: Sophia Y. Wang, Suzann Pershing, Aaron Y. LeeAbstract:Purpose of review To summarize how big data and Artificial Intelligence technologies have evolved, their current state, and next steps to enable future generations of Artificial Intelligence for ophthalmology. Recent findings Big data in health care is ever increasing in volume and variety, enabled by the widespread adoption of electronic health records (EHRs) and standards for health data information exchange, such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources. Simultaneously, the development of powerful cloud-based storage and computing architectures supports a fertile environment for big data and Artificial Intelligence in health care. The high volume and velocity of imaging and structured data in ophthalmology and is one of the reasons why ophthalmology is at the forefront of Artificial Intelligence research. Still needed are consensus labeling conventions for performing supervised learning on big data, promotion of data sharing and reuse, standards for sharing Artificial Intelligence model architectures, and access to Artificial Intelligence models through open application program interfaces (APIs). Summary Future requirements for big data and Artificial Intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of Artificial Intelligence by promoting standards for data labels, data sharing, Artificial Intelligence model architecture sharing, and accessible code and APIs.
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Big data requirements for Artificial Intelligence.
Current opinion in ophthalmology, 2020Co-Authors: Sophia Y. Wang, Suzann Pershing, Aaron Y. LeeAbstract:To summarize how big data and Artificial Intelligence technologies have evolved, their current state, and next steps to enable future generations of Artificial Intelligence for ophthalmology. Big data in health care is ever increasing in volume and variety, enabled by the widespread adoption of electronic health records (EHRs) and standards for health data information exchange, such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources. Simultaneously, the development of powerful cloud-based storage and computing architectures supports a fertile environment for big data and Artificial Intelligence in health care. The high volume and velocity of imaging and structured data in ophthalmology and is one of the reasons why ophthalmology is at the forefront of Artificial Intelligence research. Still needed are consensus labeling conventions for performing supervised learning on big data, promotion of data sharing and reuse, standards for sharing Artificial Intelligence model architectures, and access to Artificial Intelligence models through open application program interfaces (APIs). Future requirements for big data and Artificial Intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of Artificial Intelligence by promoting standards for data labels, data sharing, Artificial Intelligence model architecture sharing, and accessible code and APIs.
Chengjie Zheng - One of the best experts on this subject based on the ideXlab platform.
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Artificial Intelligence in glaucoma
Current Opinion in Ophthalmology, 2019Co-Authors: Chengjie Zheng, Aakriti Garg, Thomas V Johnson, Michael V. BolandAbstract:Purpose of reviewThe use of computers has become increasingly relevant to medical decision-making, and Artificial Intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current Artificial Intelligence methods and their applications, to h
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Artificial Intelligence in glaucoma
Current opinion in ophthalmology, 2019Co-Authors: Chengjie Zheng, Aakriti Garg, Thomas V Johnson, Michael V. BolandAbstract:The use of computers has become increasingly relevant to medical decision-making, and Artificial Intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current Artificial Intelligence methods and their applications, to help the practicing ophthalmologist understand their potential impact on glaucoma care. Techniques used in Artificial Intelligence can successfully analyze and categorize data from visual fields, optic nerve structure [e.g., optical coherence tomography (OCT) and fundus photography], ocular biomechanical properties, and a combination thereof to identify disease severity, determine disease progression, and/or recommend referral for specialized care. Algorithms have become increasingly complex in recent years, utilizing both supervised and unsupervised methods of Artificial Intelligence. Impressive performance of these algorithms on previously unseen data has been reported, often outperforming standard global indices and expert observers. However, there remains no clearly defined gold standard for determining the presence and severity of glaucoma, which undermines the training of these algorithms. To improve upon existing methodologies, future work must employ more robust definitions of disease, optimize data inputs for Artificial Intelligence analysis, and improve methods of extracting knowledge from learned results. Artificial Intelligence has the potential to revolutionize the screening, diagnosis, and classification of glaucoma, both through the automated processing of large data sets, and by earlier detection of new disease patterns. In addition, Artificial Intelligence holds promise for fundamentally changing research aimed at understanding the development, progression, and treatment of glaucoma, by identifying novel risk factors and by evaluating the importance of existing ones.
Suzann Pershing - One of the best experts on this subject based on the ideXlab platform.
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Big data requirements for Artificial Intelligence.
Current opinion in ophthalmology, 2020Co-Authors: Sophia Y. Wang, Suzann Pershing, Aaron Y. LeeAbstract:Purpose of review To summarize how big data and Artificial Intelligence technologies have evolved, their current state, and next steps to enable future generations of Artificial Intelligence for ophthalmology. Recent findings Big data in health care is ever increasing in volume and variety, enabled by the widespread adoption of electronic health records (EHRs) and standards for health data information exchange, such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources. Simultaneously, the development of powerful cloud-based storage and computing architectures supports a fertile environment for big data and Artificial Intelligence in health care. The high volume and velocity of imaging and structured data in ophthalmology and is one of the reasons why ophthalmology is at the forefront of Artificial Intelligence research. Still needed are consensus labeling conventions for performing supervised learning on big data, promotion of data sharing and reuse, standards for sharing Artificial Intelligence model architectures, and access to Artificial Intelligence models through open application program interfaces (APIs). Summary Future requirements for big data and Artificial Intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of Artificial Intelligence by promoting standards for data labels, data sharing, Artificial Intelligence model architecture sharing, and accessible code and APIs.
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Big data requirements for Artificial Intelligence.
Current opinion in ophthalmology, 2020Co-Authors: Sophia Y. Wang, Suzann Pershing, Aaron Y. LeeAbstract:To summarize how big data and Artificial Intelligence technologies have evolved, their current state, and next steps to enable future generations of Artificial Intelligence for ophthalmology. Big data in health care is ever increasing in volume and variety, enabled by the widespread adoption of electronic health records (EHRs) and standards for health data information exchange, such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources. Simultaneously, the development of powerful cloud-based storage and computing architectures supports a fertile environment for big data and Artificial Intelligence in health care. The high volume and velocity of imaging and structured data in ophthalmology and is one of the reasons why ophthalmology is at the forefront of Artificial Intelligence research. Still needed are consensus labeling conventions for performing supervised learning on big data, promotion of data sharing and reuse, standards for sharing Artificial Intelligence model architectures, and access to Artificial Intelligence models through open application program interfaces (APIs). Future requirements for big data and Artificial Intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of Artificial Intelligence by promoting standards for data labels, data sharing, Artificial Intelligence model architecture sharing, and accessible code and APIs.
