Diabetic retinopathy (DR) is a condition that progressively affects the microvasculature, commonly seen in individuals with diabetes, which is a leading cause of visual impairment across the globe. Approximately one-third of people with diabetes will eventually develop DR, and it remains a public health issue because the retinal damage is irreversible if the disease continues to go undiagnosed and untreated. Although various screening methods exist, including fundus photography and optical coherence tomography (OCT), they are often limited as a result of availability and expertise required for diagnosis and management. Recent advancements in artificial intelligence (AI) are transforming DR screening and diagnosis. AI has demonstrated the potential to automate retinal image evaluation with accuracies that, in some studies, approach or exceed existing standards of care; however, reported performance varies across algorithms, datasets, and clinical settings, highlighting the need for cautious interpretation and further validation in diverse populations. Machine learning (ML) & deep learning (DL) models, particularly convolutional neural networks (CNNs), have been found to have excellent performance in identifying DR-related abnormalities, including mild, moderate, and severe DR, and diabetic macular edema, at sensitivities and specificities exceeding traditional screening. AI tools under various names, such as IDx-DR, EyeArt, and Google's AI algorithm, are quick and price-effective tests using an AI solution for early diagnosis of DR in either developed or developing economies. In addition, AI can also support predictive analytics, risk stratification, and treatment decision-making, so that ophthalmologists can provide patients with better management options. Importantly, AI also comes with its own challenges, such as data harmonization and regulatory approval for deploying AI in clinical care. This review will focus on the existing literature on the applications of AI in diagnosing and management of DR and will outline the importance of this innovation.

Lung cancer continues to be the foremost cause of cancer-related mortality globally, with non-small cell lung cancer (NSCLC) accounting for nearly 85% of cases. Historically, the prognosis for NSCLC was grim, but advancements in targeted therapies and immunotherapy are transforming treatment paradigms. This review encapsulates significant developments in molecularly targeted agents and immune checkpoint inhibitors, emphasising their clinical efficacy, resistance mechanisms, and innovative therapeutic combinations. A thorough literature search was conducted across PubMed, ClinicalTrials.gov, and other global intellectual property databases to highlight pivotal advancements in lung cancer therapies. Each section focuses on therapies targeting key molecular drivers such as Epidermal Growth Factor Receptor (EGFR), Anaplastic Lymphoma Kinase (ALK), and others, alongside immunotherapies targeting critical pathways like PD-1 and PD-L1. In genetically defined NSCLC subsets, targeted therapies such as Osimertinib and Lorlatinib have significantly enhanced progression-free survival. Similarly, immune checkpoint inhibitors like Nivolumab have redefined care standards, particularly in PD-L1-positive tumours. Nevertheless, challenges such as acquired resistance persist, prompting ongoing exploration of combination strategies and next-generation agents. This article also reviews pivotal clinical trials and newly filed patents, reflecting current and emerging commercial interests. The integration of molecular diagnostics and precision therapies is reshaping lung cancer treatment, with prospective directions aiming to overcome resistance and broaden access to innovative therapies worldwide, including from India and other developing regions.

Nowadays, diabetic wounds are a major concern in the medical and animal fields due to delayed wound healing, as reactive oxygen species play a crucial role in the healing process. Pro-inflammatory molecules, such as tumor necrosis factor (TNF-α), matrix metalloproteinase 9 (MMP-9), and interleukin (IL)-1 β, are most prevalent in diabetic wounds, along with anti-inflammatory mediators and cytokines, which exhibit dysregulated macrophage responses. Although various plants exhibit strong anti-inflammatory, antibacterial, and antidiabetic properties, only a few have been used as effective therapeutic agents for diabetic wounds. More plants need to be explored for their effects on diabetic wounds. Alstonia scholaris (L.) R. Br. is Devil's tree, which is a rich source of various alkaloids, such as tubotaiwine, picralinal, and 16-formyl-5-methoxystrictamine, making it a potential therapeutic agent against diabetic wounds. The relevant articles showing the pharmacological effects of the tree, A. scholaris (L.) R. Br. were searched in search engines like Google Scholar, Web of Science databases, and PubMed. It also shows numerous biological properties, such as antioxidant, anti-inflammatory, antibacterial, antifungal, stimulant, carminative, stomachic, hemostatic, inhibitory cytokine, wound-healing, hypoglycemic, improving collagen and hydroxyproline synthesis, repair of skin tears, increasing granulation strength, and helping in wound contraction in models. More research on A. scholaris is needed to prove it as a potential therapeutic regimen against diabetic wounds. Therefore, this review highlights the pharmacological properties and biological effects of A. scholaris, demonstrating its potential as a candidate for the treatment of diabetic wounds.

Differences among individuals in susceptibility to adverse drug reactions are strongly influenced by genetic variation affecting drug metabolism, transport, immune response, and pharmacodynamics pathways, presenting both challenges and opportunities for drug discovery and development. Genetic and genomic variation can modify pharmacokinetics, pharmacodynamics, immunemediated toxicity, treatment resistance, and biomarker-defined therapeutic benefit, making pharmacogenetics and pharmacogenomics central components of precision medicine. The primary aim of this review is to synthesize how pharmacogenetics and pharmacogenomics function as enabling technologies across the pharmaceutical and clinical domains, with emphasis on biomarker-guided drug development, patient stratification, safety optimization, regulatory translation, precision oncology, sex-related variability, population diversity, artificial intelligence, and multi-omics integration. The review also elucidates the distinction between single-gene pharmacogenetics and genome-wide pharmacogenomics and discusses the practical requirements, procedures, advantages, and challenges associated with implementation. Future progress will depend on harmonized standards, diverse population datasets, preemptive testing linked to electronic health records, explainable artificial intelligence, multi-omics validation, and equitable access to genomic prescribing tools, ensuring that precision therapeutics improve drug safety and efficacy across populations.

The review is devoted to the use of artificial intelligence (AI) in scientific research and development to create new or repurpose authorized drug products, as well as to the use of AI-based solutions to discover new biomarkers and shorten the time to diagnosis of various diseases. The following areas of AI application are considered: the search for new pharmacologically active substances, the development of formulations and drug production technology, preclinical trials, and intelligent diagnostics (identification of new biomarkers; development of software products to interpret research results and increase diagnostic accuracy). Examples of AI use by leading pharmaceutical companies and a list of the most popular AI models in drug development are provided. The revolutionary contribution of AI in drug discovery lies in reducing the time to identify new drug candidate molecules by more rapidly identifying potential biotargets, performing virtual screening, optimizing promising candidates based on predictive data on pharmacokinetic and toxicological profiles, and searching for the optimal way to synthesize potential drugs. In addition, another area of AI application is the development of drug-delivery devices and systems that improve patient compliance and usability. This paper presents examples of AI use in intelligent diagnostics that prove their high accuracy and time efficiency compared to conventional methods of diagnostics, risk assessment, and prognosis in oncology, cardiology, and other areas of medicine. The implementation of AI technologies in medicine is intensifying, raising questions of ethics, the quality and adequacy of data, the effectiveness and safety of results for patients, personnel competence and readiness for change, as well as issues related to intellectual property rights.

Artificial Intelligence (AI) addresses many complex problems by rapidly imitating human cognitive abilities. In the pharmaceutical industry, AI is transforming the processes of discovery, formulation, nanomedicine development, and manufacturing. These applications significantly reduce cost, time, and human error. A snapshot of AI applications in the pharmaceutical sector is presented, highlighting their advantages over traditional practices. Key applications include predictive modeling for drug design, the development of intelligent nanocarriers, and adaptive learning with feedback-based process improvements for production. Additionally, AI enhances quality assurance, streamlines research and development, and supports better decision-making during clinical and manufacturing processes. By integrating AI into their workflows, pharmaceutical companies can increase productivity, improve therapeutic quality, and accelerate the development of innovative products, as intelligent systems play an increasingly vital role in modern drug development and healthcare delivery.