Innovative AI Tool Accelerates and Enhances Diagnosis of Endocrine Cancers

A groundbreaking artificial intelligence (AI) application is set to transform the diagnosis of endocrine cancers by enabling healthcare professionals to identify these complex malignancies more rapidly and with greater precision. Demonstrated at the upcoming ENDO 2025 conference in San Francisco, this publicly accessible AI system leverages advanced deep learning architectures such as EfficientNet and ResNet to analyze a variety of medical imaging data, including CT scans, MRI, ultrasonography, and histopathology images.

Developed by researchers led by Jansi Rani Sethuraj from the University of Texas Health Science Center, the tool is designed to democratize expert-level diagnostics. It can be utilized on basic internet-connected devices like smartphones, ensuring wider availability, especially in resource-limited settings. The AI models exhibited diagnostic accuracies exceeding 99% in validation tests across various endocrine cancer types affecting organs such as the thyroid, ovaries, pancreas, pituitary, and adrenal glands.

This innovation results from a collaborative effort involving the curating of diverse, anonymized datasets from six continents by researchers Ramya and Kavin Elangovan of AIM Doctor in Houston. The application’s rapid processing capability—analyzing each image in less than a second—makes it highly practical for real-time clinical decision-making.

Global healthcare professionals have assessed the system's performance and usability, confirming its potential for widespread clinical adoption. By providing swift and reliable diagnostic support, this AI tool promises to reduce errors, accelerate treatment planning, and ultimately improve patient outcomes. As emphasized by project lead Elangovan Krishnan, this technology represents a significant stride toward equitable access to cutting-edge cancer diagnostics worldwide.

This initiative underscores the importance of scalable solutions in fighting endocrine cancers, which cause millions of deaths annually, by making advanced diagnostics more accessible and efficient for clinicians around the globe.