In recent decades, as a subclass of biomaterials, biologically sensitive nanoparticles have attracted increased scientific interest. Many of the demands for physiologically responsive nanomaterials in applications involving the human body cannot be met by conventional technologies. Due to the field’s importance, considerable effort has been expended, and biologically responsive nanomaterials have achieved remarkable success thus far. This review summarizes the recent advancements in biologically responsive nanomaterials and their applications in biosensing and molecular imaging. The nanomaterials change their structure or increase the chemical reaction ratio in response to specific bio-relevant stimuli (such as pH, redox potentials, enzyme kinds, and concentrations) in order to improve the signal for biologically responsive diagnosis. We use various case studies to illustrate the existing issues and provide a clear sense of direction in this area. Furthermore, the limitations and prospects of these nanomaterials for diagnosis are also discussed.

Carbon-based nanomaterials, including carbon nanotubes, carbon nanospheres, and carbon nanofibers, are becoming a research hotspot due to their unique structure and good mechanical, thermal, electrical, optical, and chemical properties. With the development of material synthesis technology, they can be functionalized and used in various fields such as energy, environment, and biomedicine. In particular, stimuli-responsive carbon-based nanomaterials have stood out in recent years because of their smart behavior. Researchers have applied carbon-based nanomaterials to different disease treatments based on their stimulus-response properties. In this paper, based on stimuli-responsive carbon-based nanomaterials’ morphology, we categorize them into carbon nanotubes, carbon nanospheres, and carbon nanofibers according to their morphology. Then, their applications in probes, bioimaging, tumor therapy, and other fields are discussed. Finally, we address the advantages and disadvantages of carbon-based stimuli-responsive nanomaterials and discuss their future perspective.

This review explores the synthesis, drug loading, and surface modifications of metal–organic frameworks (MOFs), highlighting their role in improving cancer immunotherapy and paving the way for safer and more effective treatments.

Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer’s disease (AD), Parkinson’s disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.