Background: Multimodal imaging plays an important role in the diagnosis of brain disorders. Neurological disorders need to be diagnosed at an early stage for their effective treatment as later, it is very difficult to treat them. If possible, diagnosing at an early stage can be much helpful in curing the disease with less harm to the body. There is a need for advanced and multimodal imaging techniques for the same. This paper provides an overview of conventional as well as modern imaging techniques for brain diseases, specifically for tumor imaging. In this paper, different imaging modalities are discussed for tumor detection in the brain along with their advantages and disadvantages. Conjugation of two and more than two modalities provides more accurate information rather than a single modality. They can monitor and differentiate the cellular processes of normal and diseased condition with more clarity. The advent of molecular imaging, including reporter gene imaging, has opened the door of more advanced noninvasive detection of brain tumors. Due to specific optical properties, semiconducting polymer-based nanoparticles also play a pivotal role in imaging tumors.Objective: The objective of this paper is to review nanoparticles-mediated brain imaging and disease prognosis by conventional as well as modern modal imaging techniques.Conclusion: We reviewed in detail various medical imaging techniques. This paper covers recent developments in detail and elaborates a possible research aspect for the readers in the field.

Artificial intelligence (AI) is increasingly influencing healthcare, with growing relevance in infectious diseases and clinical microbiology. This review summarizes current applications of machine learning (ML), deep learning (DL), and large language models (LLMs) in these areas while critically evaluating their strengths and limitations. Although AI has shown promising performance in image analysis, molecular diagnostics, risk prediction, and clinical decision support, its routine implementation remains constrained by limited external validation, heterogeneous datasets, algorithmic bias, and challenges in generalizability and explainability. The review also discusses AI’s emerging role in scientific writing and the ethical and practical considerations associated with its clinical adoption. Overall, current evidence suggests that AI should be regarded as a complementary decision-support technology whose safe and effective integration depends on rigorous validation, high-quality data, and appropriate clinical oversight.