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Artificial Intelligence Homepage

Sectra showcases work-in-progress, AI functionality at RSNA Gives radiologist greater control over findings

Scalable AI utilization insights from Montefiore at RSNA Starting with patients at risk for respiratory failure

NYU releases biggest ever MR data set in AI Facebook collaboration With fastMRI, acceleration of imaging by factor of four 'already possible'

Subtle Medical closes RSNA with CE mark and FDA clearance of PET AI solution Speeds up scans by factor of four, enhanced image quality

Infervision showcases new AI concepts at RSNA Detecting four different conditions on one chest scan

Canon debuts AI for image reconstruction and 1.5T MR at RSNA Advanced Intelligent Clear-IQ Engine and Vantage Orian

Where will AI make its first major market impact in radiology? Four radiology experts share their views at RSNA

Aidoc and ACR announce partnership for AI in imaging Establishing a registry to better understand AI in the clinical setting

How will radiologists access AI? Integrating machine learning into existing business structure and radiologist workflow

Arterys touts cloud-native platform and regulatory approval in 98 countries AI capabilities with 'unmatched' security

The pulse of medical AI: An innovation prognosis

By Elad Walach and Dr. Yoni Goldwasser

The transformative impact of AI on healthcare has stopped being a point for debate. It's not a question of "if," but of "how," and "how fast."

The field of medical imaging is a prime example of one of the many healthcare subfields that will feel the effects of AI on their workflow in the near future. AI will bring immense gain at each stage in the imaging value chain, which will no doubt be followed by the challenge of adoption for hospitals and radiologists. AI-focused startups and multinationals alike are both seizing the opportunities presented by this growing area of innovation.
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THE (LEADER) IN MEDICAL IMAGING TECHNOLOGY SINCE 1982. SALES-SERVICE-REPAIR

Special-Pricing Available on Medical Displays, Patient Monitors, Recorders, Printers, Media, Ultrasound Machines, and Cameras.This includes Top Brands such as SONY, BARCO, NDS, NEC, LG, EDAN, EIZO, ELO, FSN, PANASONIC, MITSUBISHI, OLYMPUS, & WIDE.


AI will bring value at various points in the healthcare value chain
The imaging value chain can be broken down into several stages, with AI contributing in each:

  • Scheduling, administration, patient management, and workflow optimization. Given the current inefficiencies utilizing imaging technologies, the complex interface between various providers and the changing regulatory environment regarding these applications, AI offers a much-needed way of optimizing patient management. Companies like HealthLevel are trying to help radiologists improve efficiencies by providing BI and clinical metrics. Other solutions from the HIS/RIS space will continue to come into play in the coming years.

  • Pre-scan (e.g., patient positioning): While choosing the correct protocols and ensuring proper patient positioning is ostensibly the responsibility of physicians and technicians, AI algorithms can help prevent errors, improper care, and other difficulties. Bay Labs and Butterly iQ, for instance, use AI to reduce operator dependence in ultrasounds.

  • In-scan: One study's results often lead to further studies, wasting resources and prolonging time to diagnosis and care. Through live image processing, AI algorithms could help predict the need to employ new protocols or conduct further studies.

  • Post-scan/interpretation: Here is where AI's potential to streamline workflows is particularly valuable. AI can help radiologists prioritize caseloads – reducing, in some settings, more than 90% of diagnosis time for time-sensitive cases. Some AI companies try to target a broad set of clinical use cases (e.g., Aidoc, Zebra Medical, etc.), while others offer deep specialty around specific solutions.

  • Predictive analytics/biomarkers – Companies like Quantib and IcoMetrix are trying to find new biomarkers for complex cases like Alzheimer's, helping radiologists spot patterns invisible to the naked eye.

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