Tag

Radiology

All articles tagged with #radiology

technology2 days ago•4 min saved

Apple Studio Display XDR Brings DICOM Imaging for Radiology

Apple's Studio Display XDR now supports DICOM medical imaging presets and a Medical Imaging Calibrator, enabling radiologists to view diagnostic radiology images on a high-end monitor; the calibrator is pending FDA clearance and should be available in the US soon. Pre-orders begin March 4 with a March 11 launch. The display features mini-LED backlighting, 2,304 local dimming zones, 2000 nits peak brightness, a 1,000,000:1 contrast ratio, and a 120Hz refresh rate, with a starting price of $3,299, offering a more affordable option than many dedicated medical imaging monitors.

via MacRumors|

#dicom #fda-clearance #medical-imaging-calibrator

health1 month ago•6 min saved

AI-aided mammograms cut risk of aggressive breast cancer in large Swedish trial

A Swedish Lancet study of over 100,000 women found AI-assisted reading of mammograms increased cancer detection and reduced interval cancers by 12%, suggesting fewer aggressive cancers later and potential health-cost savings; the AI acted as a triage aid (low-risk cases read by one radiologist, high-risk by two). Limitations include single-center data and missing race/ethnicity info, with longer-term mortality and cost-effectiveness to be assessed.

via CBC|

#ai-assisted-mammography #breast-cancer #health

healthcare9 months ago•1 min saved

AI Mammography Debuts Amid Trust Concerns

AI-based mammography tools are increasingly being adopted in U.S. radiology practices, with some claiming to detect more cancers than radiologists alone, but skepticism remains among many imaging centers and radiologists about their trustworthiness and effectiveness.

via statnews.com|

#ai-mammography #cancer-detection #digital-health

health1 year ago•5 min saved

AI Revolutionizes Brain Tumor Detection with Speed and Precision

AI models using convolutional neural networks and transfer learning from tasks like camouflage detection have achieved 85.99% accuracy in distinguishing brain tumors from healthy tissue in MRI scans, nearing human performance. This study emphasizes explainability, allowing AI to highlight cancerous areas, fostering trust among radiologists and patients. While slightly less accurate than human detection, the method shows promise for AI as a transparent tool in clinical radiology.

via Neuroscience News|

#ai #brain-tumor #health

healthtech1 year ago•2 min saved

"AI's Impact on Early Breast Cancer Detection and Treatment Outcomes"

Clinics are offering a new service where mammograms are read by both radiologists and artificial intelligence models, aiming to improve accuracy in detecting breast cancer. While experts are excited about the potential of AI tools, they have concerns about their effectiveness across diverse patient populations and their impact on breast cancer survival. AI analysis can identify patterns in mammograms that may indicate cancer, but there are also challenges in differentiating certain patterns from normal breast tissue.

via The New York Times|

#ai #breast-cancer #healthtech

healthcaretechnology2 years ago•1 min saved

"Ipswich Hospital's AI-Driven Lung Cancer Detection Research"

Ipswich Hospital is participating in the LungIMPACT research study, using AI software to assist in detecting abnormalities on chest X-rays, with the aim of expediting diagnostic processes. The study, running until July 2024, will involve reviewing 9,000 GP-referred chest X-rays to assess the effectiveness of AI in prioritizing X-rays for review and potentially speeding up the time to diagnosis. Consultant radiologist Dr. James Hathorn emphasized the importance of properly researching and evidencing AI products for the future of healthcare, with final decisions remaining in the hands of clinicians.

via BBC.com|

#ai #healthcaretechnology #ipswich-hospital

health2 years ago•6 min saved

"The Cost of Mammography AI: Is It Worth the Extra Expense for Patients?"

Some radiology clinics are offering patients the option to pay for artificial intelligence (AI) analysis of their mammograms, which is not covered by insurance. While AI software has the potential to improve the detection of suspicious breast masses and lead to earlier diagnoses of breast cancer, there is ambiguity regarding its individual benefit. The FDA has authorized AI products to help detect and diagnose cancer from mammograms, but there are currently no billing codes for radiologists to charge health plans for the use of AI. The cost of AI analysis raises concerns about equity and affordability, with some experts questioning the need for additional charges for a service that could be beneficial for all women.

via CBS News|

#ai #breast-cancer #health

medical-technology2 years ago•5 min saved

Radiologists trump AI in detecting lung diseases on chest X-rays

Radiologists outperformed commercially available AI tools in accurately identifying the presence and absence of three common lung diseases on chest X-rays, according to a study published in Radiology. While AI tools showed moderate to high sensitivity rates, they produced more false-positive results than radiologists, especially when multiple findings were present or for smaller targets. The study highlights the need for further testing of AI tools in real-life clinical scenarios and emphasizes the importance of radiologists' expertise in interpreting complex chest X-rays. AI systems could serve as a valuable second opinion for radiologists but should not be autonomous in making diagnoses.

via EurekAlert|

#ai #chest-x-rays #diagnostic-accuracy

healthcare-policy2 years ago•3 min saved

"Medicare Fee Schedule Sparks Outrage and Calls for Reform with Proposed Pay Cuts"

The Centers for Medicare & Medicaid Services (CMS) has released the initial 2024 Medicare Physician Fee Schedule, proposing a pay cut for diagnostic and interventional radiology. The proposed fee schedule also suggests potentially rescinding the long-delayed Appropriate Use Criteria (AUC) Program. The preliminary changes include reductions in relative value units for various specialties, with diagnostic radiology facing a 3% cut and interventional specialists facing a 4% cut. The proposed 2024 conversion factor, used to convert RVUs into payment, represents a 3.36% reduction from the 2023 amount. The Medical Group Management Association (MGMA) expressed concerns about the cut, stating that it would further increase the gap between practice expenses and reimbursement rates. CMS is also considering doing away with the AUC Program due to challenges in implementation and potential risks. The agency is accepting comments on the proposed rule until September 11.

via Radiology Business|

#appropriate-use-criteria-program #fee-schedule #healthcare-policy

healthcare2 years ago•9 min saved

The Debate Over AI in Healthcare: Patient Care or Diagnosis?

While AI can significantly improve the accuracy of medical diagnoses, it cannot replace human doctors in holding a patient's hand, listening to their wishes, and making difficult decisions. AI can help improve the accuracy of test results and interpretation, particularly in cases where multiple factors are at play. However, doctors must be cautious in adopting AI and only do so where there is evidence to support its use.

via Daily Mail|

#accuracy #ai #diagnosis

healthcare-technology3 years ago•3 min saved

Revolutionizing Radiology with DALL-E 2 AI Image Generation.

DALL-E 2, a text-to-image generation deep learning model, has promising potential for image generation, augmentation, and manipulation in healthcare, particularly in radiology. The model has learned relevant representations of x-ray images and can create realistic x-ray images based on short text prompts. However, its capabilities for generating images with pathological abnormalities or other medical imaging modalities are still limited. Synthetic data generated by DALL-E 2 could accelerate the development of new deep learning tools for radiology and address privacy concerns related to data sharing between institutions. Further research and development are needed to fine-tune these models to medical data and incorporate medical terminology to create powerful models for data generation and augmentation in radiology research.

via Neuroscience News|

#ai-image-generation #dall-e-2 #deep-learning