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GTC ON-DEMAND

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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:

In this session, attendees will learn how to develop an AI Learning Platform for healthcare, develop initial(imaging) AI applications in specific care areas, and embed AI into devices creating "intelligent imaging systems".

In this session, attendees will learn how to develop an AI Learning Platform for healthcare, develop initial(imaging) AI applications in specific care areas, and embed AI into devices creating "intelligent imaging systems".

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Topics:
AI in Healthcare, Medical Imaging & Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8991
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Abstract:
As computers outperform humans at complex cognitive tasks, disruptive innovation will increasingly remap the familiar with waves of creative destruction. And in healthcare, nowhere is this more apparent or imminent than at the crossroads of Radiology and the emerging field of Clinical Data Science. As leaders in our field, we must shepherd the innovations of cognitive computing by defining its role within diagnostic imaging, while first and foremost ensuring the continued safety of our patients. If we are dismissive, defensive or self-motivated - industry, payers and provider entities will innovate around us achieving different forms of disruption, optimized to serve their own needs. To maintain our leadership position, as we enter the era of machine learning, it is essential that we serve our patients by directly managing the use of clinical data science towards the improvement of carea position which will only strengthen our relevance in the care process.
As computers outperform humans at complex cognitive tasks, disruptive innovation will increasingly remap the familiar with waves of creative destruction. And in healthcare, nowhere is this more apparent or imminent than at the crossroads of Radiology and the emerging field of Clinical Data Science. As leaders in our field, we must shepherd the innovations of cognitive computing by defining its role within diagnostic imaging, while first and foremost ensuring the continued safety of our patients. If we are dismissive, defensive or self-motivated - industry, payers and provider entities will innovate around us achieving different forms of disruption, optimized to serve their own needs. To maintain our leadership position, as we enter the era of machine learning, it is essential that we serve our patients by directly managing the use of clinical data science towards the improvement of carea position which will only strengthen our relevance in the care process.  Back
 
Topics:
AI in Healthcare
Type:
Keynote
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7240
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Abstract:

As computers outperform humans at complex cognitive tasks, disruptive innovation will increasingly remap the familiar with waves of creative destruction.  And in healthcare, nowhere is this more apparent or imminent than at the crossroads of Radiology and the emerging field of Clinical Data Science. As leaders in our field, we must shepherd the innovations of cognitive computing by defining its role within diagnostic imaging, while first and foremost ensuring the continued safety of our patients.  If we are dismissive, defensive or self-motivated - industry, payers and provider entities will innovate around us achieving different forms of disruption, optimized to serve their own needs.  To maintain our leadership position, as we enter the era of machine learning, it is essential that we serve our patients by directly managing the use of clinical data science towards the improvement of carea position which will only strengthen our relevance in the care process as well as in future federal, commercial and accountable care discussions. We'll explore the state of clinical data science in medical imaging and its potential to improve the quality and relevance of radiology as well as the lives of our patients.

As computers outperform humans at complex cognitive tasks, disruptive innovation will increasingly remap the familiar with waves of creative destruction.  And in healthcare, nowhere is this more apparent or imminent than at the crossroads of Radiology and the emerging field of Clinical Data Science. As leaders in our field, we must shepherd the innovations of cognitive computing by defining its role within diagnostic imaging, while first and foremost ensuring the continued safety of our patients.  If we are dismissive, defensive or self-motivated - industry, payers and provider entities will innovate around us achieving different forms of disruption, optimized to serve their own needs.  To maintain our leadership position, as we enter the era of machine learning, it is essential that we serve our patients by directly managing the use of clinical data science towards the improvement of carea position which will only strengthen our relevance in the care process as well as in future federal, commercial and accountable care discussions. We'll explore the state of clinical data science in medical imaging and its potential to improve the quality and relevance of radiology as well as the lives of our patients.

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Topics:
AI in Healthcare, Healthcare and Life Sciences, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7840
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