Computer software and artificial intelligence (AI) company RSIP Vision has released a new AI module that can pinpoint lesions and relevant anatomy during image-guided lung biopsy.
The AI module relies on a combination of AI image segmentation algorithms and computer vision to identify potential lesions and the border of the lungs on medical imaging, helping clinicians navigate lung biopsies with greater accuracy, according to RSIP Vision. This may help minimize the risk of false-positive lung nodules and intraoperative complications, the company added.
"From the radiology point of view, this new AI module is becoming an increasingly helpful tool, especially in detecting small peripheral or very proximal lung lesions that could be of clinical importance and might be missed without it," Dr. Rabeeh Fares from Tel Aviv Medical Center said in a statement.
The module can be integrated into the software of medical devices used in a variety of specialties, including orthopedics, pulmonology, and cardiology. Looking ahead, RSIP Vision may explore using the AI segmentation technology to facilitate other interventional procedures, such as lesion ablations.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
