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Artificial Intelligence Homepage

Aidoc announces $27 million in VC funding to advance AI in imaging Brings company's total funding to $40 million

New study questions patient understanding on AI in radiology Asserts that greater education and communication is required

Fredrik Palm ContextVision appoints new CEO

ACR engages in collaborations for AI development with launch of AI-LAB platform Allows radiologists to create algorithms of their own

New AI software identifies make and model of cardiac implants in seconds Speed up diagnosis and delivery of treatment for patients with faulty devices

Dicom Systems scores enterprise imaging contract with Radiology Partners Will integrate IT and clinical workflows of more than 850 provider facilities

Apple study suggests wearable technology may be useful in detecting atrial fibrillation May assist in stroke and hospitalization prevention

Nvidia unveils Clara AI platform at GPU Technology Conference Equipped with 13 state-of-the-art classification and segmentation algorithms

BSWH to install Glassbeam's CLEAN blueprint to leverage machine uptime Will include integrated CMMS software by EQ2

Beyond the hype: How practical AI is enhancing radiology Insights from Imad B. Nijim, chief information officer for MEDNAX Radiology Solutions

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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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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