Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Accurate diagnosis from histopathological biopsy samples is essential, yet manual examination is time-consuming and subject to inter-observer variability, particularly given the shortage of trained pathologists alongside the increasing number of cases. Deep learning, especially Convolutional Neural Networks (CNNs), has emerged as a powerful tool for classifying medical images by automatically extracting discriminative features from raw data. In this study, we investigate the use of the publicly available Breast Cancer Histopathological (BreakHis) image database, which contains benign and malignant... More >

Fusion of multi-modal medical images has transformed healthcare by overcoming the limitations of single-modality imaging, where modalities such as CT, MRI, PET, and SPECT provide complementary information. This review systematically traces the evolution of multi-modal medical image fusion from conventional mathematical models to state-of-the-art artificial intelligence (AI) techniques. We examine the transition from classical approaches---such as multiscale transformations, wavelet decompositions, and sparse representation---to modern deep learning methods, including convolutional neural networks, generative adversarial networks, and transformer architectures. Key limitations of existing met... More >

Long non-coding RNAs (lncRNAs) are important for plant growth, how plants respond to stress, and their overall development. However, it can be difficult to identify them accurately because they come in many structures and can look similar to coding RNAs. In this study, we introduce DeepPInc V2.0, a new tool that uses deep learning to analyze both the sequence and the secondary structure of RNAs, combining them in a DenseNet-CNN hybrid model. DeepPInc V2.0 outperforms existing tools on various plant datasets, achieving an accuracy of 94.2%, an F1-score of 0.93, and a Matthews Correlation Coefficient (MCC) of 0.88. It consistently outperforms seven leading tools in this area. Importantly, the... More >

Exorbitant expenses, lengthy development periods, and a high incidence of drug candidate attrition plague the conventional pharmaceutical R&D pipeline---a problem sometimes referred to as ``Eroom's Law.'' By radically reorganizing the discovery process, generative artificial intelligence (AI), which has emerged as a transformational force, promises to buck this tendency. Through data synthesis on key performance metrics, this review offers a thorough analysis of the effects of AI-enhanced methodologies. We explore how a new set of tools is changing the paradigm from experimental screening to in silico design. These tools include graph neural networks (GNNs)—a class of neural architectures... More >

According to the President of the United Nations, AI holds enormous promise for accelerating progress towards numerous United Nations Sustainable Development Goals (SDGs). This paper focuses on various applications of technologies such as artificial neural networks (ANN) and deep learning (DL) in the development of pharmaceutical solid dosage forms. DL is a subset of machine learning (ML) that utilizes extensive experimental data to learn through advanced methods like artificial neural networks. ANNs can analyze patient data to generate customized drug delivery regimens based on genetic and medical histories. A range of AI technologies, including neural networks, fuzzy logic, and evolutionar... More >

Low Dose Computed Tomography (LDCT) scan is modern medical imaging diagnostic technique that provides a detailed projection of internal human body tissue level structures. Even though the LDCT image quality is compromised by Gaussian-noise, which can be generated during image acquisition, this compromises the accurate diagnostic precision. The effective denoising is required to improve image quality in LDCT images. This study demonstrates that the Discrete Wavelet Transform(DWT) method shows better results, both quantitatively and visually, under varying noise intensities ($\sigma=10,20,30,$ and $40$). The DWT method decomposes the image to multiresolution subbands (approximation, and detail... More >

Biomedical Informatics and Smart Healthcare are helping to enhance and improve modern medicine and healthcare facilities by promoting technology-enhanced care and patient-centric approaches. This editorial primarily discusses the latest advancements in the fields of artificial intelligence, big data analytics, and IoT-enabled medical devices that are redefining medical workflows, diagnostics, and healthcare delivery. As the field evolves, biomedical informatics has enabled personalized patient treatment, real-time health tracking, and data-driven decision-making. The editorial also addresses ethical and regulatory concerns associated with this medical transformation, including issues related... More >

Medical imaging is an essential and valuable tool in modern medicine for providing essential details on the internal structures and functioning of the human body. Although very useful, the raw images obtained from medical imaging systems usually contain different types of artifacts and noise that might hide some essential diagnostic information. In this paper, the author details the conventional and non-conventional methods as well as sophisticated deep learning research methods used in improving the quality of healthcare images. The paper also delves into strategies designed to elevate the visual quality and interpretability in medical diagnostics. The paper includes some latest case studie... More >