Liver Cancer Detection: A Review of Artificial Intelligence Techniques

One of the most innovative ways to identify liver cancer is using deep learning. In medical image analysis, Convolutional Neural Networks (CNNs) reliably detect potential tumors in CT, MRI, and ultrasonography. The potential for improved patient outcomes and early detection is great with this research. The use of recurrent neural networks (RNNs) to interpret time-series data, including blood tests, offers an alternate method. RNNs may help with early risk prediction and personalized monitoring plan development. The interpretability of AI models, data quality, and seamless integration into healthcare processes are still problems, though. Advances in data synthesis, explainable AI (XAI), and userfriendly interfaces are addressing these challenges. Future prospects appear bright thanks to emerging technologies like Generative Adversarial Networks (GANs) and customized medical approaches that leverage several data sources. Collaboration between researchers, healthcare providers, and technology companies is necessary to accelerate innovation and ensure that it is more broadly available.