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Abstract:

The increasing availability of large medical imaging data resources with associated clinical data, combined with the advances in the field of machine learning, hold large promises for disease diagnosis, prognosis, therapy planning and therapy monitoring. As a result, the number of researchers and companies active in this field has grown exponentially, resulting in a similar increase in the number of papers and algorithms. A number of issues need to be addressed to increase the clinical impact of the machine learning revolution in radiology. First, it is essential that machine learning algorithms can be seamlessly integrated in the clinical workflow. Second, the algorithm should be sufficiently robust and accurate, especially in view of data heterogeneity in clinical practice. Third, the additional clinical value of the algorithm needs to be evaluated. Fourth, it requires considerable resources to obtain regulatory approval for machine learning based algorithms. In this workshop, the ACR and MICCAI Society will bring together expertise from radiology, medical image computing and machine learning, to start a joint effort to address the issues above.

The increasing availability of large medical imaging data resources with associated clinical data, combined with the advances in the field of machine learning, hold large promises for disease diagnosis, prognosis, therapy planning and therapy monitoring. As a result, the number of researchers and companies active in this field has grown exponentially, resulting in a similar increase in the number of papers and algorithms. A number of issues need to be addressed to increase the clinical impact of the machine learning revolution in radiology. First, it is essential that machine learning algorithms can be seamlessly integrated in the clinical workflow. Second, the algorithm should be sufficiently robust and accurate, especially in view of data heterogeneity in clinical practice. Third, the additional clinical value of the algorithm needs to be evaluated. Fourth, it requires considerable resources to obtain regulatory approval for machine learning based algorithms. In this workshop, the ACR and MICCAI Society will bring together expertise from radiology, medical image computing and machine learning, to start a joint effort to address the issues above.

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Topics:
AI in Healthcare, Medical Imaging & Radiology
Type:
Panel
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8897
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Abstract:
"AI is transforming healthcare" is the buzz around every news alert these daysbut is it true? Where is AI and deep learning affecting healthcare and how is it impacting the medical imaging space? Join a thought leadership panel as government, industry and academic experts discuss the real calibration of the spaceseparating reality from the noise to explore how deep learning is advancing clinical practice, including advancements to overcome data and regulatory challenges.
"AI is transforming healthcare" is the buzz around every news alert these daysbut is it true? Where is AI and deep learning affecting healthcare and how is it impacting the medical imaging space? Join a thought leadership panel as government, industry and academic experts discuss the real calibration of the spaceseparating reality from the noise to explore how deep learning is advancing clinical practice, including advancements to overcome data and regulatory challenges.  Back
 
Topics:
Leadership and Policy in AI, AI in Healthcare
Type:
Panel
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7246
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