Description
Accurate interpretation of medical images and the creation of comprehensive narrative reports are crucial for patient care, yet they impose significant demands on clinical specialists. Recent advancements in artificial intelligence (AI), particularly in what is termed multimodal generative medical image interpretation (GenMI), present opportunities to automate elements of this intricate process. In this presentation, we present the research being pursued in our lab towards progress made and the challenges faced in creating AI systems designed to generate medical reports based on radiological images. Our primary focus is on radiology - a field characterized by substantial reporting requirements and ongoing research initiatives. We examine the benefits and applications of emerging models for medical report generation while proposing a new framework for employing MM-GenAI, that empowers both clinicians and their patients. Preliminary studies indicate that MM-GenAI has the potential to eventually achieve performance levels comparable to those of human experts in generating reports across various fields, including radiology, pathology, and dermatology. Nevertheless, significant challenges persist regarding model accuracy validation, transparency, and capturing nuanced insights. If implemented thoughtfully, MM-GenAI could significantly support clinicians by enhancing care quality, enriching medical education, alleviating workloads, broadening access to specialties, and offering real-time expertise. Ultimately, we underscore both the promising possibilities and key obstacles associated with developing multimodal generative AI that works alongside human experts to ensure dependable medical report writing, based on automatic interpretation of radiological Images.| Period | 14 Feb 2025 |
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| Event title | 2025 International Conference on Intelligent Control, Computing and Communications (IC3) |
| Event type | Conference |
| Location | Mathura, UP, IndiaShow on map |
| Degree of Recognition | International |