Intelligent Rare Medical Event Detection
  • Author(s): Soham Mhatre; Dr. P. D. Adkar
  • Paper ID: 1717648
  • Page: 1479-1485
  • Published Date: 13-05-2026
  • Published In: Iconic Research And Engineering Journals
  • Publisher: IRE Journals
  • e-ISSN: 2456-8880
  • Volume/Issue: Volume 9 Issue 11 May-2026
Abstract

Identifying rare diseases is one of the toughest hurdles in modern medicine be-cause information is scarce and symptoms are often confusingly diverse, which fre-quently leads to long and stressful diag-nostic delays for patients. To solve this, we created CliniFlow AI, an intelligent platform designed to help doctors spot these rare conditions much earlier using a unique "brain" called Anomaly-Aware Adaptive Multimodal Fusion (A²MF), al-lowing it to recognize over 230 rare dis-eases even when data is extremely lim-ited. The system connects the dots by ana-lyzing clinical text with BioBERT, spot-ting abnormalities in medical images with a customized ResNet-50, and tracking a patient's health history over time using LSTM networks. By flagging unusual pat-terns through Anomaly Detection and learning from a small number of examples via Few-Shot Learning, CliniFlow AI pro-vides doctors with clear risk levels and diagnostic insights, making it a powerful and easy-to-scale tool for real-world hos-pitals. Beyond simple automation, this framework acts as a second pair of eyes that stays sharp during high-stakes medi-cal screenings where every minute counts. It effectively bridges the gap between massive amounts of raw hospital data and the specialized, actionable knowledge cli-nicians need to save lives.

Keywords

Rare Disease Detection, Mul-timodal Learning, Clinical Decision Sup-port System, Medical Image Analysis, Bi-oBERT, Few-Shot Learning, Anomaly De-tection, Explainable AI.

Citations

IRE Journals:
Soham Mhatre, Dr. P. D. Adkar "Intelligent Rare Medical Event Detection" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 1479-1485

IEEE:
Soham Mhatre, Dr. P. D. Adkar "Intelligent Rare Medical Event Detection" Iconic Research And Engineering Journals, 9(11)