Cerbrec’s Post

**Game Changer in Medical Diagnosis 🎯: AI Uncertainty is NOT Uncertain🧠💡** Medical imaging can be a murky area where different experts might interpret the same thing differently. AI can lend a helping hand here, but the problem was it typically provided only one 'concrete' answer. Not anymore! Meet [Tyche], the new kid on the block, courtesy of researchers from MIT, the Broad Institute and Massachusetts General Hospital! 👏 Tyche's superpower is capturing the uncertainty in a medical image, 📸 which helps clinicians make more informed decisions. It's all about options with Tyche! You get to specify how many interpretations or 'segmentations' you want and choose the most relevant. The best part? It doesn't need to be retrained for each new task. Just use it right out of the box to figure out what those lung X-ray or brain MRI anomalies mean. 🏥 By putting ambiguity and context into the picture, Tyche’s helping to #revolutionize #medicalimaging 💡🔬🚀. Here's more to this game-changing https://zurl.co/IWvK And remember, our AI-rockstars at Echomotion are always ready to help simplify the complexities of AI for your biz,

**Game Changer in Medical Diagnosis 🎯: AI Uncertainty is NOT Uncertain🧠💡** Medical imaging can be a murky area where different experts might interpret the same thing differently. AI can lend a helping hand here, but the problem was it typically provided only one 'concrete' answer. Not anymore! Meet [Tyche], the new kid on the block, courtesy of researchers from MIT, the Broad Institute and Massachusetts General Hospital! 👏 Tyche's superpower is capturing the uncertainty in a medical image, 📸 which helps clinicians make more informed decisions. It's all about options with Tyche! You get to specify how many interpretations or 'segmentations' you want and choose the most relevant. The best part? It doesn't need to be retrained for each new task. Just use it right out of the box to figure out what those lung X-ray or brain MRI anomalies mean. 🏥 By putting ambiguity and context into the picture, Tyche’s helping to #revolutionize #medicalimaging 💡🔬🚀. Here's more to this game-changing https://zurl.co/IWvK And remember, our AI-rockstars at Echomotion are always ready to help simplify the complexities of AI for your biz,

SATURDAY SCIENCE "Artificial intelligence models often play a role in medical diagnoses, especially when it comes to analyzing images such as X-rays. However, studies have found that these models don’t always perform well across all demographic groups, usually faring worse on women and people of color. These models have also been shown to develop some surprising abilities. In 2022, MIT researchers reported that AI models can make accurate predictions about a patient’s race from their chest X-rays — something that the most skilled radiologists can’t do. That research team has now found that the models that are most accurate at making demographic predictions also show the biggest “fairness gaps” — that is, discrepancies in their ability to accurately diagnose images of people of different races or genders. The findings suggest that these models may be using “demographic shortcuts” when making their diagnostic evaluations, which lead to incorrect results for women, Black people, and other groups, the researchers say." https://lnkd.in/e5Gcmu3Y

There is a new and troubling study into bias (and its impact) in AI models for medical diagnosis, specifically vision-language models for medical imaging. The study examines the algorithmic fairness of state-of-the-art vision-language foundation models in chest X-ray diagnosis across five globally-sourced datasets. The paper is in preprint and from researchers at MIT, University of California San Diego, and University of Washington. The study primarily examined CheXzero, which is a self-supervised foundation model used in medical imaging. The findings show that compared to board-certified radiologists, the foundation models examined consistently under-diagnose marginalised groups, with even higher rates of under-diagnosis seen in the intersections of these groups (the combination of gender and race being a particularly high rate). The demographic biases were present over a range of pathologies and demographic attributes. With so much discussion on bias in LLMs (eg coverage of the Gemini model's generation of human images), I thought this is a particularly important example to highlight, as it looks at the specific impact of bias in a field that is not only mission-critical, but life-critical. The paper is available here: https://lnkd.in/gnH4UnqN #ai #bias #medicine

Artificial intelligence models often play a role in medical diagnoses, especially when it comes to analyzing images such as X-rays. However, studies have found that these models don’t always perform well across all demographic groups, usually faring worse on women and people of color.These models have also been shown to develop some surprising abilities. In 2022, MIT researchers reported that AI models can make accurate predictions about a patient’s race from their chest X-rays — something that the most skilled radiologists can’t do.That research team has now found that the models that are most accurate at making demographic predictions also show the biggest “fairness gaps” — that is, di ...

To the untrained eye, a medical image like an MRI or X-ray appears to be a murky collection of black-and-white blobs. It can be a struggle to decipher where one structure (like a tumor) ends and another begins. When trained to understand the boundaries of biological structures, AI systems can segment (or delineate) regions of interest that doctors and biomedical workers want to monitor for diseases and other abnormalities. Instead of losing precious time tracing anatomy by hand across many images, an artificial assistant could do that for them.The catch? Researchers and clinicians must label countless images to train their AI system before it can accurately segment. For example, you’d ne ...

Te Papa Hauora: AI in Health good opportunity to look at the advancements and challenges facing health in regards to the use and influence of AI. Interesting range of ideas and projects, and insight across the health sphere, including education, surgical services, pathology amongst others. Diagnostic AI as routine use in colonoscopy, prediction and surgical need in bowel cancer as part of pre-operative processes; intraoperative use in terms of keyhole surgery and anatomic recognition using computer vision, automated surgical skill assessment; postoperative monitoring - continuous sensors, identification of complications, remote outpatient monitoring were highlighted in Prof. Tim Eglington's presentation. Great to see the critique and analysis offered by Dr Saxon Connor - and recognition of healthcare role in environmental impact. What do we actually know about AI in terms of source - is it clean and where is it coming from? We need to recognise the impact of using AI with large datasets- impact of big tech and knowledge transfer from more traditional sources such as universities to private tech companies. The presentation met the key focus of triggering thought and raising questions, not just providing descriptions of current or anticipated developments.

𝐑𝐚𝐝𝐢𝐨𝐥𝐨𝐠𝐲 𝐀𝐈 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐑𝐞𝐩𝐨𝐫𝐭 𝟐𝟎𝟐𝟒-𝟐𝟎𝟑𝟐 - 𝐒𝐚𝐧 𝐆𝐥𝐨𝐛𝐚𝐥 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐓𝐨 𝐊𝐧𝐨𝐰 𝐭𝐡𝐞 𝐆𝐥𝐨𝐛𝐚𝐥 𝐒𝐜𝐨𝐩𝐞 𝐚𝐧𝐝 𝐃𝐞𝐦𝐚𝐧𝐝 𝐨𝐟 𝐑𝐚𝐝𝐢𝐨𝐥𝐨𝐠𝐲 𝐀𝐈 𝐌𝐚𝐫𝐤𝐞𝐭. 𝐑𝐞𝐪𝐮𝐞𝐬𝐭 𝐟𝐨𝐫 𝐒𝐚𝐦𝐩𝐥𝐞 𝐏𝐃𝐅: https://lnkd.in/dwTZAv9A The #radiology AI market refers to the growing demand for artificial intelligence (AI) solutions in medical imaging and diagnostics. These solutions leverage machine learning algorithms to analyze medical images and assist radiologists in making more accurate diagnoses and developing more effective treatment plans. Talk of #artificial intelligence (AI) has been running rampant in radiology circles. Sometimes referred to as machine learning or deep learning, AI, many believe, can and will optimize radiologists' workflows, facilitate quantitative radiology, and assist in discovering genomic markers. The San Global Research report includes an overview of the development of the Radiology #AI industry chain, the market status of Neurology (Image Display, Model Display), Cardiovascular (Image Display, Model Display), and key enterprises in developed and developing market, and analysed the cutting-edge technology, patent, hot applications and market trends of Radiology AI. *𝗕𝘆 𝗧𝘆𝗽𝗲: Image Display, Model Display *𝗕𝘆 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Neurology, Cardiovascular, Lung, Liver *𝗕𝘆 𝗥𝗲𝗴𝗶𝗼𝗻𝘀: North America, Europe, Asia-Pacific, South America, Middle East & Africa *𝗕𝘆 𝗞𝗲𝘆 𝗣𝗹𝗮𝘆𝗲𝗿𝘀: Arterys (Acquired by Tempus Labs), Aidoc, GE, IBM, Medtronic, Qure.ai, Siemens #RadiologyAI #MedicalImaging #HealthcareAI #RadiologyTechnology #AIinHealthcare #DiagnosticImaging #DeepLearning #MachineLearning #MedicalTechnology #RadiologyInnovation #HealthTech #ArtificialIntelligence #MedicalDiagnosis #RadiologySolutions #ClinicalDecisionSupport #HealthcareInnovation #DigitalHealth #PrecisionMedicine #MedicalResearch #ImagingTechnology #RadiologyInsights #MedicalAI #HealthcareTech #RadiologyAdvancements #DiagnosticTools #ImagingAnalysis #RadiologyExperts #AIApplications #HealthcareFuture #RadiologyRevolution

Study reveals why AI models that analyze medical images can be biased by Anne Trafton , Massachusetts Institute of Technology "Artificial intelligence models often play a role in medical diagnoses, especially when it comes to analyzing images such as X-rays. However, studies have found that these models don't always perform well across all demographic groups, usually faring worse in women and people of color." More information: The limits of fair medical imaging AI in real-world generalization, Nature Medicine (2024). DOI: 10.1038/s41591-024-03113-4 Journal information: Nature Medicine https://lnkd.in/dcp3V4kS

Artificial intelligence unpredictably impacts radiologist performance - Health IT Analytics #AIinRadiology: A Study on the Impact of Artificial Intelligence on Radiologist Performance #StudyOverview: A recent study explores how artificial intelligence can affect the performance of radiologists in interpreting medical images. #ResearchFindings: The study found that the use of AI tools can lead to both improvements and challenges for radiologists, with varying impacts on accuracy and efficiency. #Improvements: AI tools can help radiologists in detecting abnormalities and making accurate diagnoses, leading to better patient outcomes. #Challenges: However, the study also identified challenges such as overreliance on AI, which can potentially decrease the radiologist's ability to interpret images independently. #Recommendations: To maximize the benefits of AI in radiology, it is essential ai.mediformatica.com #research #health #radiologist #clinicians #radiologists #tools #technology #this #healthcare #medical #analytics #artificialintelligence #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/43A4YJd)