Emerging Innovations in Imaging AI

Thrilled to share our latest publication in the Journal of Personalized Medicine! Our study, AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography, dives deep into how advanced AI models like DenseNet121 and ResNet50 can revolutionize pulmonary diagnostics. By analyzing over 108,000 chest X-rays, our approach achieved a remarkable 94% AUC in detecting conditions such as pneumothorax and oedema, even outperforming expert radiologists. We also explored the integration of NLP techniques, like Named Entity Recognition and Sentiment Analysis, to enhance clinical workflows, reducing processing times by 60% and annotation errors by 75%. Our findings highlight the transformative potential of AI in medical imaging, paving the way for more accurate and efficient diagnostics. Curious to learn more? I invite you to read the full article and join the conversation on how AI is shaping the future of healthcare. #AI #HealthcareInnovation #MedicalImaging #DeepLearning #Radiology #MdpiOpenAccess https://lnkd.in/gG9GsFyb

𝗡𝗲𝘄 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱! Medical imaging is packed with hidden clinical biomarkers, but privacy hurdles and data scarcity often keep this treasure trove locked away from AI innovation. Frustrating, right? That’s exactly what inspired me and Abdullah Hosseini to ask: Can we generate synthetic medical images that not only look real, but also preserve the critical biomarkers clinicians rely on? So, we dove in. Using cutting-edge diffusion models fused with Swin-transformer networks, we generated synthetic images across three modalities—radiology (chest X-rays), ophthalmology (OCT), and histopathology (breast cancer slides). The big question: 𝗗𝗼 𝘁𝗵𝗲𝘀𝗲 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗶𝗺𝗮𝗴𝗲𝘀 𝗸𝗲𝗲𝗽 𝘁𝗵𝗲 𝘀𝘂𝗯𝘁𝗹𝗲, 𝗱𝗶𝘀𝗲𝗮𝘀𝗲-𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗶𝗻𝘁𝗮𝗰𝘁? • Our diffusion models faithfully preserved key biomarkers—like lung markings in X-rays and retinal abnormalities in OCT—across all datasets. • Classifiers trained only on synthetic data performed nearly as well as those trained on real images, with F1 and AUC scores hitting 0.8–0.99. • No statistically significant difference in diagnostic performance—meaning synthetic data could stand in for real data in many AI tasks, while protecting patient privacy. This work shows synthetic data isn’t just a lookalike—it’s a powerful, privacy-preserving tool for research, clinical AI, and education. Imagine sharing and scaling medical data without the headaches of privacy risk or limited access! Read the full paper: https://lnkd.in/eW6TM9H2 Get the code & datasets: https://lnkd.in/ek4wSkg3 #AI #Innovation #SyntheticData #DiffusionModels #MedicalImaging #HealthcareInnovation #DigitalHealth #Frontiers #WeillCornell #HealthTech #HealthcareAI #PrivacyPreservingAI #GenerativeAI #Biomarkers #MachineLearning #Qatar #MENA #MiddleEast #NorthAfrica #MENAIRegion #MENAInnovation #UAE #UnitedArabEmirates #SaudiArabia #KSA #Egypt AI Innovation Lab Weill Cornell Medicine Weill Cornell Medicine - Qatar Cornell Tech Cornell University

MIT and Harvard Medical School researchers just unlocked interactive 3D medical image analysis with language! Medical imaging AI has long been limited to rigid, single-task models that require extensive fine-tuning for each clinical application. 𝗩𝗼𝘅𝗲𝗹𝗣𝗿𝗼𝗺𝗽𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘃𝗶𝘀𝗶𝗼𝗻-𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗲𝗻𝗮𝗯𝗹𝗲𝘀 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲, 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗳 𝟯𝗗 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝘀𝗰𝗮𝗻𝘀 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀. 1. Unified multiple radiology tasks (segmentation, volume measurement, lesion characterization) within a single, multimodal AI model. 2. Executed complex imaging commands like “compute tumor growth across visits” or “segment infarcts in MCA territory” without additional training. 3. Matched or exceeded specialized models in anatomical segmentation and visual question answering for neuroimaging tasks. 4. Enabled real-time, interactive workflows, allowing clinicians to refine analysis through language inputs instead of manual annotations. Notably, I like that the design includes native-space convolutions that preserve the original acquisition resolution. This addresses a common limitation in medical imaging where resampling can degrade important details. Excited to see agents being introduced more directly into clinician workflows. Here's the awesome work: https://lnkd.in/ggQ4YGeX Congrats to Andrew Hoopes, Victor Ion Butoi, John Guttag, and Adrian V. Dalca! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW