Mastodawn

Researchers have developed microscopic glass bead lasers—measuring between 0.1mm and 0.01mm—capable of acting as highly sensitive optical biosensors. These #microlasers can detect materials at an unprecedented scale, identifying individual molecules and single atomic ions.
#OpticalPhysics #Biophysics #Biochemistry #MedicalDiagnostics #Photonics #sflorg
https://www.sflorg.com/2026/03/phy03252601.html

First microlasers capable of detecting individual molecules and ions could one day aid diagnosis

Microlasers are tiny glass beads measuring around just 0.1 mm (the width of a human hair) to 0.01mm – (the length of a single bacterium).

Smidmart Dec 8

The future will be faster, sharper & life-saving. 🧠🔍
AI vision isn’t just improving medical diagnostics —
it’s redefining what’s possible.
Smidmart- https://zurl.co/gXilq
#Smidmart #AIVision #MedicalDiagnostics #FutureOfHealthcare #AIDrivenCare #DeepLearning #MedicalImaging

SCAB Pharmacy Limited Nov 12

𝗪𝗼𝗿𝗹𝗱 𝗣𝗮𝘁𝗵𝗼𝗹𝗼𝗴𝘆 𝗗𝗮𝘆
🔍 Did you know every life-saving diagnosis starts in a lab? This #InternationalPathologyDay (Nov 12), we celebrate the experts behind the scenes—pathologists and lab scientists who make accurate diagnosis and treatment possible!

🌟 𝗧𝗵𝗲𝗺𝗲 𝟮𝟬𝟮𝟱: “𝗧𝗵𝗲 𝗚𝗹𝗼𝗯𝗮𝗹 𝗣𝗮𝘁𝗵𝗼𝗹𝗼𝗴𝘆 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲” — 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝘃𝗶𝘁𝗮𝗹 𝗿𝗼𝗹𝗲 𝗶𝗻 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝘄𝗼𝗿𝗹𝗱𝘄𝗶𝗱𝗲.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:
✅ Early disease detection
✅ Precise treatment guidance
✅ Monitoring patient progress
✅ Protecting public health
✅ Driving medical breakthroughs

Let’s thank these healthcare heroes and inspire future generations!
📢 Share your story or tag a lab professional who’s made a difference.

#IPD2025 #PathologyMatters #HealthcareHeroes #MedicalDiagnostics #EarlyDetection #PrecisionMedicine #GlobalHealth #LabScience #FutureScientists #HealthAwareness

Newsramp Sep 20, 2025

CNSide Diagnostics achieves critical CMS laboratory certification, expanding diagnostic capabilities for CNS cancer patients and enhancing healthcare innovation potential #MedicalDiagnostics #CancerResearch

ScienceOpen Aug 19, 2025

'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

Development of an Artificial Intelligence System for Distinguishing Malignant from Benign Soft-Tissue Tumors Using Contrast-Enhanced MR Images

Introduction: 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. Methods: 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. Results: 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. Conclusion: 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. 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.

ScienceOpen

Sanjay Mohindroo May 9, 2025

A must-read post that sparks lively discussions on tech innovations in computer vision. #ComputerVision, #ImageProcessing, #TechInnovation, #MedicalDiagnostics, #AutonomousDriving, #SecuritySystems, #FutureTech, #AI, #Innovation, #Discussion, #Community, #ArtTech, #AgTech, #Privacy, #Data, #TechEthics, #Collaboration
https://medium.com/@sanjay.mohindroo66/vision-in-focus-the-art-and-science-of-computer-vision-image-processing-88614812f17b

Vision in Focus: The Art and Science of Computer Vision & Image Processing.

An insightful blog post on computer vision and image processing, highlighting its impact on medical diagnostics, autonomous driving, and security systems. Computer vision and image processing have…

Medium

Amelia Cervera 🧬Apr 19, 2025

A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians.

#GenAI #MedicalDiagnostics

https://www.nature.com/articles/s41746-025-01543-z

A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians - npj Digital Medicine

While generative artificial intelligence (AI) has shown potential in medical diagnostics, comprehensive evaluation of its diagnostic performance and comparison with physicians has not been extensively explored. We conducted a systematic review and meta-analysis of studies validating generative AI models for diagnostic tasks published between June 2018 and June 2024. Analysis of 83 studies revealed an overall diagnostic accuracy of 52.1%. No significant performance difference was found between AI models and physicians overall (p = 0.10) or non-expert physicians (p = 0.93). However, AI models performed significantly worse than expert physicians (p = 0.007). Several models demonstrated slightly higher performance compared to non-experts, although the differences were not significant. Generative AI demonstrates promising diagnostic capabilities with accuracy varying by model. Although it has not yet achieved expert-level reliability, these findings suggest potential for enhancing healthcare delivery and medical education when implemented with appropriate understanding of its limitations.

Nature

Science News Oct 24, 2023

#CollectiveIntelligence can help reduce medical misdiagnoses. Researchers @mpib_berlin @CNRsocial_ @NTNUnorway introduced a fully automated solution using knowledge engineering methods @HACID #MedicalDiagnostics https://www.mpib-berlin.mpg.de/press-releases/collective-intelligence-in-medicine
https://nachrichten.idw-online.de/2023/10/24/collective-intelligence-can-help-reduce-medical-misdiagnoses

Collective intelligence can help reduce medical misdiagnoses

Researchers from the Max Planck Institute for Human Development, the Institute for Cognitive Sciences and Technologies (ISTC), and the Norwegian University of Science and Technology developed a collective intelligence approach to increase the accuracy of medical diagnoses. Their work was recently presented in the journal PNAS.

idw_online Oct 24, 2023

#CollectiveIntelligence can help reduce medical misdiagnoses. Researchers @mpib_berlin @CNRsocial_ @NTNUnorway introduced a fully automated solution using knowledge engineering methods @HACID #MedicalDiagnostics https://www.mpib-berlin.mpg.de/press-releases/collective-intelligence-in-medicine
https://nachrichten.idw-online.de/2023/10/24/collective-intelligence-can-help-reduce-medical-misdiagnoses

Collective intelligence can help reduce medical misdiagnoses

Researchers from the Max Planck Institute for Human Development, the Institute for Cognitive Sciences and Technologies (ISTC), and the Norwegian University of Science and Technology developed a collective intelligence approach to increase the accuracy of medical diagnoses. Their work was recently presented in the journal PNAS.

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HACID project Aug 1, 2023

Demonstration in #MedicalDiagnostics starts from interesting preliminary results, in which we aggregate responses from multiple users, thanks to the collaboration with Human Dx (https://www.humandx.org). Collective diagnoses achieve accuracy up to 80%, much better than the average individual results

The Human Diagnosis Project

Human Dx aims to empower anyone with the world’s collective medical insight.