# Advancements in Real-Time Oncology Diagnosis: Harnessing AI and Image Fusion Techniques

arXiv: https://arxiv.org/abs/2503.11332 · HTML: https://mlsanity.com/paper/2503.11332/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 70 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations](https://mlsanity.com/paper/2402.07192/) (2024)
- [Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data](https://mlsanity.com/paper/2312.02195/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey on Surgical Digital Twin](https://mlsanity.com/paper/2512.00019/) (2025)

## Abstract (arXiv)

Real-time computer-aided diagnosis using artificial intelligence (AI), with images, can help oncologists diagnose cancer with high accuracy and in an early phase. We reviewed real-time AI-based analyzed images for decision-making in different cancer types. This paper provides insights into the present and future potential of real-time imaging and image fusion. It explores various real-time techniques, encompassing technical solutions, AI-based imaging, and image fusion diagnosis across multiple anatomical areas, and electromagnetic needle tracking. To provide a thorough overview, this paper discusses ultrasound image fusion, real-time in vivo cancer diagnosis with different spectroscopic techniques, different real-time optical imaging-based cancer diagnosis techniques, elastography-based cancer diagnosis, cervical cancer detection using neuromorphic architectures, different fluorescence image-based cancer diagnosis techniques, and hyperspectral imaging-based cancer diagnosis. We close by offering a more futuristic overview to solve existing problems in real-time image-based cancer diagnosis.