Scholarlink

With regards to the clinical implementation of artificial intelligence in medicine, there is still a gap between the discrete outcomes generated by AI algorithms and their application in real-life scenarios. To resolve this mismatch, we suggest the creation of one production-ready diagnostic platform that will combine several AI services. Our contribution doesn’t lie in algorithmic innovation but in cutting through the engineering and clinical challenges of integrating the best medical AI features to become a seamless cohesive picture. A unified AI system for chest X-rays is a case in point: our systems integration comprises classifying AI, interpretability algorithms such as Grad-CAM, clinical insights through Gemini AI, high contrast digital audio to aid users who are deaf or hard of hearing with Text-to-Speech and location analytics enabled by Maps API. Integrating modalities of AI presented here was to conquer problems with getting multiple AI modules working in harmony, sorting out clinical work flows, making the whole thing accessible, and setting up the computing architecture that lets it scale. Well-known evaluation on a database of 6,266 chest X-rays shows that our system is remarkably accurate, boasting a 96.4.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-Powered Chest X-Ray Diagnosis System with Explainability and Real-Time Clinical Support

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Talluru Venkata Jaswanth Chowdary,
  • Sunkari Ravindra,
  • Settipalli Mahivardhan,
  • Jonnada Praisee Surya Raj,
  • Kiran Gopal Pedireddy,
  • J. Divya Udayan

摘要

With regards to the clinical implementation of artificial intelligence in medicine, there is still a gap between the discrete outcomes generated by AI algorithms and their application in real-life scenarios. To resolve this mismatch, we suggest the creation of one production-ready diagnostic platform that will combine several AI services. Our contribution doesn’t lie in algorithmic innovation but in cutting through the engineering and clinical challenges of integrating the best medical AI features to become a seamless cohesive picture. A unified AI system for chest X-rays is a case in point: our systems integration comprises classifying AI, interpretability algorithms such as Grad-CAM, clinical insights through Gemini AI, high contrast digital audio to aid users who are deaf or hard of hearing with Text-to-Speech and location analytics enabled by Maps API. Integrating modalities of AI presented here was to conquer problems with getting multiple AI modules working in harmony, sorting out clinical work flows, making the whole thing accessible, and setting up the computing architecture that lets it scale. Well-known evaluation on a database of 6,266 chest X-rays shows that our system is remarkably accurate, boasting a 96.4.