A global study explores patient attitudes toward #MedicalAI. Acceptance is higher among those in good health and with tech familiarity. Most want #AI as a supportive tool, not a decision-maker: http://t1p.de/medicalAI
#AIinMedicine #HealthCare #Radiology
📷V. Stepanov / iStock
'Development of an Artificial Intelligence System for Distinguishing Malignant from Benign #SoftTissueTumors Using Contrast-Enhanced MR Images' - a Karger: #Oncology article on #ScienceOpen -
🔗 https://www.scienceopen.com/document?vid=7f67a6c5-d186-4285-8e5f-2d56a6adfda0
#MedicalDiagnostics #MRI #AIinMedicine #Radiomics #DigitalHealth
<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d2621084e267"> <b> <i>Introduction:</i> </b> The integration of artificial intelligence (AI) into orthopedics has enhanced the diagnosis of various conditions; however, its use in diagnosing soft-tissue tumors remains limited owing to its complexity. This study aimed to develop and assess an AI-driven diagnostic support system for magnetic resonance imaging (MRI)-based soft-tissue tumor diagnosis, potentially improving accuracy and aiding radiologists and orthopedic surgeons. <b> <i>Methods:</i> </b> An experienced orthopedic oncologist and radiologist annotated 720 images from 77 cases (41 benign and 36 malignant soft-tissue tumors). Eleven tumor subtypes were identified and classified into benign and malignant groups based on histological diagnosis. Utilizing the standard machine learning classifier pipeline, we examined and down-selected imaging protocols and their predominant radiomic features within the tumor’s three-dimensional region to differentiate between benign and malignant tumors. Among the scan protocols, contrast-enhanced T1-weighted fat-suppressed images showed the most accurate classification based on radiomic features. We focused on the two-dimensional features from the largest tumor boundary surface and its neighboring slices, leveraging texture-based radiomic and deep convolutional neural network features from a pretrained VGG19 model. <b> <i>Results:</i> </b> The test data comprised 44 contrast-enhanced images (22 benign and 22 malignant soft-tissue tumors) containing six malignant and five benign subtypes distinct from the training data. We compared expert and nonexpert human performances against AI by assessing malignancy detection and the time required for classification. The AI model showed comparable accuracy (AUC 0.91) to that of radiologists (AUC 0.83) and orthopedic surgeons (AUC 0.73). Notably, the AI model processed data approximately 400 times faster than its human counterparts, showcasing its capacity to significantly boost diagnostic efficiency. <b> <i>Conclusion:</i> </b> We developed an AI-driven diagnostic support system for MRI-based soft-tissue tumor diagnosis. While additional refinement is necessary for clinical applications, our system has exhibited promising potential in differentiating between benign and malignant soft-tissue tumors based on MRI. </p><p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d2621084e296">MRI is invaluable for the qualitative diagnosis of soft-tissue tumors, but radiologists face challenges despite advancements in imaging technologies. This study aimed to develop and evaluate an AI-driven diagnostic support system for MRI-based soft-tissue tumor diagnosis. The study used 720 images from 77 cases. Using a standard machine learning classifier pipeline, we examined and down-selected imaging protocols and their predominant radiomic features within each tumor’s three-dimensional region to differentiate between benign and malignant tumors. Contrast-enhanced and T2-weighted images were identified as candidates for developing a machine learning model for malignancy classification. We focused on the two-dimensional features from the largest tumor boundary surface and its neighboring slices, leveraging texture-based radiomic and deep convolutional neural network features from a pretrained VGG19 model. A support vector machine classifier trained with contrast-enhanced images demonstrated the best performance in preliminary validation investigations. Validation was performed using test data comprising 44 contrast-enhanced images. The AI model exhibited comparable accuracy (AUC 0.91) to that of radiologists (AUC 0.83) and orthopedic surgeons (AUC 0.73). The AI model processed data approximately 400 times faster than its human counterparts, demonstrating its capacity to significantly enhance diagnostic efficiency. While additional refinement is necessary for clinical applications, our system has exhibited promising potential in differentiating between benign and malignant soft-tissue tumors based on MR images. </p>
AI in Healthcare - Trends, Uses, and Future
Artificial Intelligence in Healthcare is one of the most promising areas in the healthcare field, and we will discuss the current trends, uses, and its future.
I just say no to a doctor who wants to use AI to record a consultation with me. I don't visit doctors often but when I have been asked for permission I have said no.
The reasons I have for not giving permission are:
1. No written privacy policy and statement about how my data will be used, stored and protected.
2. No demonstration of how the AI has been rigorously developed and tested by professionals who are properly qualified to eliminate bias.
3. No written promise that I can review the notes about the consult so that I know what is in my medical record and fix any inaccuracies about what I said, did etc.
There is a lot of bias against older women among health professionals, whether explicit or subconscious. The risk of bias negatively impacting me is very high.
In the period from July to December 2024 the health industry in Australia reported the most number of data breaches of any industry according to the Office of the Australian Information commissioner.
#AIinMedicine #DataProtection #DataPrivacy #Australia #Medicine
https://www.abc.net.au/news/2025-08-06/what-to-know-about-doctors-using-ai-digital-scribes/105590878
Could the future of Parkinson’s diagnosis lie in your ears? Ear wax might just be the game-changing tool doctors have been waiting for. #ParkinsonsAwareness #Neuroscience #AIinMedicine
https://geekoo.news/ear-wax-could-revolutionize-parkinsons-screening/
Could a simple ear swab revolutionize the diagnosis of Parkinson’s disease? Researchers found that volatile compounds in ear wax, paired with AI, might provide a painless, early-screening tool for one of neurology’s toughest challenges.
Technische Universität München
Newsramp
ScienceOpen
nextdigitalhealth
Yvonne Perkins
Christos Argyropoulos MD, PhD, FASN 🇺🇸 0kale/acc
Christos Argyropoulos MD PhD
Genomics Daily 🧬🤖
guIA - guía a la IA
Geekoo