Aidoc | Clinical AI
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Latest posts
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22 Hospitals, One Stroke Network: The Role of AI at the BG Klinikum Unfallkrankenhaus Berlin
Prof. Dr. med. Sven Mutze and Alexander Böhmcker on using AI to support connected stroke care in Germany. What happens when stroke imaging from 22 hospitals arrives fast — sometimes even at the same time — and urgent cases need to be prioritised for radiologist review? At the BG…
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22 Kliniken, ein Schlaganfall-Netzwerk: Die Rolle von KI am BG Klinikum Unfallkrankenhaus Berlin
Prof. Dr. med. Sven Mutze und Alexander Böhmcker über den Einsatz von KI zur Unterstützung einer vernetzten Schlaganfallversorgung in Deutschland. Was passiert, wenn Schlaganfall-Bildgebung aus 22 Kliniken in kurzer Zeit eintrifft – manchmal sogar gleichzeitig – und die…
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A Routine Scan Revealed a Hidden Threat: How Aidoc Helped WellSpan Health Act Fast for Carolyn
At 82, Carolyn was completing another round of cancer treatment and looking forward to what came next: more time with family, camping trips, community events, and the active life she shares with her husband of 62 years. She has always been someone who keeps moving. “I like to…
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How 3 FDA Breakthrough Designations Accelerate Patient Care and Scale Diagnostic AI
Developing clinical and diagnostic AI solutions used to take years, if not decades. That time has a cost. The longer that life-changing solutions stay in development, the smaller the opportunity to help current patients who might’ve had better outcomes. That’s why bringing…
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How Leading Health Systems Scale AI Across Service Lines
Many health systems already have some level of AI in place. But oftentimes, the existing AI tools are fragmented — creating more silos, more vendors, more integration work, and more burden than value. To truly scale clinical AI, health systems need to focus on systemwide…
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Operationalizing Clinical AI: A Path to Clinician Buy-In
In healthcare, using AI solutions can make a life-changing difference for patients. But if clinicians don’t feel comfortable or excited to use the tools, they won’t use them effectively. To get the most value out of clinical AI, clinicians must be able to actively use it in the…
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The Future of Rural Healthcare Access Is Expertise at Scale
In many rural communities, seeing a specialist can mean driving hours for an appointment or waiting weeks for care. And as hospitals struggle to recruit clinicians, patients are often left with even fewer options. The challenge won’t ease anytime soon. The Health Resources and…
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Solving for distance: How Aidoc is supporting radiology across New Zealand
New Zealand’s healthcare system faces a challenge familiar to many countries with dispersed populations: hospitals don’t always have a radiologist on site, and the specialists who can interpret complex, time-critical scans are often based hours away. For a patient presenting…
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How Better Systems Can Support Faster Diagnoses
Healthcare has spent decades improving patient safety, yet diagnostic delays and errors affect hundreds of thousands of patients each year. More than a decade ago, the National Academy of Medicine identified diagnostic error as a major patient safety concern. Since then,…
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How WellSpan Health Built a Diagnostic AI Layer to Provide a Better Experience for Patients and Providers
The use of AI in healthcare is often framed as a trade-off: it makes things easier for clinicians, but patient experience may decline. Or, the patient experience improves, but frontline care teams take on more work, leading to stress and burnout. A recent Scottsdale Institute…