Streamlining Medical Imaging with AI

Check out recently released healthcare foundation models from Microsoft: MedImageInsight - Embedding model for advanced image analysis, including classification and similarity search in medical imaging. - Streamlines workflows across radiology, pathology, ophthalmology, dermatology, and other modalities. - Researchers can use embeddings directly or build adapters for specific tasks. - Enables tools to automatically route imaging scans to specialists or flag potential abnormalities. - Enhances efficiency and patient outcomes. - Supports Responsible AI safeguards like out-of-distribution detection and drift monitoring to maintain stability and reliability. CXRReportGen - Multimodal AI model for generating detailed, structured reports from chest X-rays. - Incorporates current and prior images along with key patient information. - Highlights AI-generated findings directly on images to align with human-in-the-loop workflows. - Accelerates turnaround times while enhancing diagnostic precision. - Supports diagnosis of a wide range of conditions—from lung infections to heart problems. - Addresses the most common radiology procedure globally. MedImageParse - Precise image segmentation model covering X-rays, CT scans, MRIs, ultrasounds, dermatology images, and pathology slides. - Can be fine-tuned for specific applications like tumor segmentation or organ delineation. - Enables developers to build AI tools for sophisticated medical image analysis.

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

I’m encouraged by the “Unlocking the Value of AI in Healthcare” report’s clear articulation of industry pain points—and the AI strategies ready to address them. Dasha Tyshlek has conducted in-depth research on this topic and shared an impressive publication on healthcare problems and opportunities to solve them with various AI solutions. ⭐ A full report: https://lnkd.in/g4ZY5Ci2 These are my key takeaways from the report: 1️⃣ AI Addressing: Manual, Time-Consuming Workflows Clinicians spend countless hours on chart reviews, administrative tasks, and manual measurements. AI-powered computer-vision applications can automate image-based readings (for example, TB skin tests or dermatology scans), while intelligent task automation handles referrals, billing queries, and regulatory reporting. 2️⃣ AI Addressing: Diagnostic Delays & Accuracy Gaps Missed or late diagnoses—driven by human error, limited specialist availability, or slow lab turnaround—can be life-threatening. Deep-learning models for radiology and pathology now flag anomalies with expert-level sensitivity, and predictive-analytics engines identify high-risk patients by correlating vitals, labs, and historic trends. 3️⃣ AI Addressing: Inefficient Clinical Trial & R&D Processes Enrollment, stratification, and protocol design in clinical trials remain labor-intensive. AI-driven cohort-discovery platforms rapidly match eligible patients to studies by mining EHRs, and generative AI accelerates protocol drafting and regulatory submissions. By aligning each challenge with a targeted AI intervention—from federated learning and NLP to computer vision and predictive modeling—the report provides a practical blueprint for healthcare organizations. Last year, AccelIQ.digital had the privilege of partnering with Dasha StratCraft Partners to conduct research and develop an AI product proposal for a TB screening app for the Methodist Hospital. 🔍 The TST Challenge based on this summary: Houston Methodist’s annual TB surveillance program administers approximately 7,200 Tuberculin Skin Tests (TSTs) and 7,200 Interferon-Gamma Release Assays (IGRAs) each year, with TSTs used preferentially for new hires and routine follow-up. However, an estimated 10–20% of employees fail to return for their Traditional Tuberculin Skin Tests (TSTs) leading to: ➡️ Lost productivity, as employees step away from critical duties ➡️ Repeat visits and extra costs when tests are invalid, and inaccurate reads ➡️ Scheduling headaches for tight hospital rosters Our Mobile AI Solution Proposed Direction 🎯 Eliminates the second-visit mandate by using computer vision to read and interpret the test site in real time with 95% accuracy 🎯 Integrates seamlessly into hospital workflows, pushing results directly to HR and clinician dashboards 🎯 Reduces indirect costs—cutting labor hours, no-show repeat tests, and staffing disruptions #AIHealthcare #Innovation #ValueCreation