Review on artificial intelligence supported breast cancer diagnosis for effective treatment planning

Breast cancer (BC) is recognized as a major cause of women’s mortality around the world, emphasizing the need for progress in early and accurate diagnosis and customized medications. Recent progress and application of Artificial Intelligence (AI) in the medical section is increasing, particularly in BC early screening and molecular profiling, along with customized treatment planning. The AI-based clinical research findings state that it improves the accuracy in diagnosis, reduces inter-observer fluctuations, and enables predictive modelling for treatment responses, eventually strengthening precision oncology. However, several research shortages exist, like the lack of wide-ranging, dissimilar datasets; inadequate enlightened clinical studies of AI models; and the lack of translucent, comprehensible algorithms suitable for practical usages. Additional concerns comprehend algorithmic bias, altering regulatory backgrounds, and data safety, which together obstruct typical use. This review carefully evaluates existing AI uses in BC care, highlighting treatment planning, and examines new leanings like understandable AI and amalgamated learning, as well as virtual reality simulations.