Background: The digital healthcare sector in India is rapidly transforming, driven by strategic government initiatives and technological advancements. In 2020, the market was valued at approximately $1.5 billion and is projected to grow at a compound annual growth rate of ~25% over the next five years.

Methodology: The study employed a descriptive and analytical approach, reviewing existing literature and data on the applications and implications of AI, mHealth, and GIS technologies in healthcare..

Result: The article highlighted the rapid growth of India's digital health market, driven by the adoption of telemedicine, mobile health, and electronic health records, alongside increased investments and internet penetration. Additionally, it also raised concerns about bias, transparency, and accountability in these technologies, urging the development of robust digital infrastructure, including Digital Health IDs, Health Facility Registries, and Healthcare Professionals Registries, as well as policy changes like the effective implementation of Personal Data Protection Bill and updates to the Information Technology Act.

Conclusion: India's healthcare market is at a critical juncture, where effective management of the ongoing digital transformation can vastly improve access and outcomes for millions. By tackling current challenges and embracing technological advancements, India could set a global standard in digital healthcare, ensuring equitable, high-quality care for all citizens, regardless of location or socio-economic status. The vision of a fully integrated digital healthcare system is not just possible but an impending reality that, with the right strategies and collaborations, could be realized within the next decade.

The incorporation of advanced telemedicine technologies is helping artificial intelligence transform remote healthcare in the enhancement of patient care, diagnostics, monitoring, and overall medical treatment. This review examines how AI has transformed virtual healthcare with regard to patient engagement and connectivity, real-time monitoring of health status, and the accuracy of diagnosis. Key applications of AI, such as AI-enabled diagnostic systems, predictive analytics, and teleconsultation platforms, are reviewed for their strengths in overcoming the limitations of the traditional models of remote healthcare. This review consists of case studies on the applications of AI in different healthcare domains, such as cardiac monitoring, diabetes management, mental health teletherapy, and dermatology. It also looks into the ethical and regulatory challenges that arise, including bias in AI, data privacy, and accountability, in a way that emphasizes the necessity for robust regulatory frameworks in safeguarding patient safety. Future directions for AI innovation include such emerging technologies as 5G, blockchain, and IoMT, among others, that “will usher in a new era of remote healthcare delivery.”

Introduction: Adverse drug reactions (ADRs) in Antiretroviral Treatment (ART) are influenced by multiple potentiators related to the patient, the disease, the drug, the environment and medical treatment, these ADRs are highly prevalent and are identified as an important risk factor that predisposes patients to ADRs. It was considered necessary to determine the demographic, social, and clinical factors associated with ADRs from antiretrovirals in HIV-positive patients, who were treated by the specialized comprehensive care program in a primary health care model.

Methodology: Observational, cross-sectional, analytical, and retrospective study with a population of patients on antiretroviral therapy in a primary care program. The outcome evaluated was adverse drug reactions vs. sociodemographic, pharmacological and clinical factors. For the statistical analysis, univariate, bivariate and multivariate analyses were performed, where a multiple binary logistic regression was used for explanatory purposes.

Results: A total of 5406 records of patients with antiretroviral therapy were analyzed, the prevalence of ADR was 16.68%, the multivariate analysis showed that the variables that increase the probability of ADR are age, education, area of residence, pharmacological group, HDL cholesterol levels, adherence, persistence, change of two or more times of ARV and treatment time.

Conclusion: Antiretrovirals, as well as the risk factors that are mainly associated with the occurrence of ADRs in this study, contribute to health professionals at all levels to anticipate, identify and minimize ADR, as well as to understand the need for close follow-up and monitoring to avoid the occurrence of serious ADRs.

Antitumor drug therapies encounter substantial costs and intricate challenges, imposing a financial strain on patients and potentially leading to serious adverse effects. These issues have prompted a shift towards personalized precision medicine, although the increased workload for clinicians limits its full implementation. Machine learning (ML) offers innovative solutions to these challenges. By effectively integrating and analysing large clinical datasets, ML can develop models to predict potential treatment-related risks for patients and optimize dosing regimens, thereby improving efficacy and reducing adverse effects. Additionally, ML can evaluate drug efficacy, providing empirical support for personalized treatments. This review highlights the research progress in ML for antitumor drug therapies and examines its crucial role in advancing personalized precision medicine. It is expected that ML will deliver more accurate, efficient, and cost-effective treatment options for patients while providing strong support for clinicians in refining treatment decisions, making it an essential tool in cancer therapy.

Artificial intelligence (AI) is designed to mimic human intelligence in machines. The growth of information technology and advancement in the computing power of computers provided a great platform for progress in many pharmaceutical industry and healthcare sectors. Leading to the consolidation of the pharmaceutical, and healthcare industries with AI companies. AI is used in various departments of the pharmaceutical sector such as drug discovery, development, target identification, manufacturing process, dosage design, clinical trial design, and many more. There are several challenges and limitations of AI that must be addressed by the pharmaceutical industry before its adoption and successful integration into various processes. The present article is focused on Artificial Neural Networks in the pharmaceutical sector, Drug design and discovery, drug repurposing, research and development, pharmaceutical product development, manufacturing process, quality assurance and quality controls, and some challenges and prospects of AI.

Protein and peptide-based therapeutics hold immense potential for treating various diseases, including cancer, neurodegenerative disorders, and metabolic conditions. However, rapid degradation, poor bioavailability, short half-life, and early clearance limit their clinical application. Several protein and peptide modifications and drug delivery systems (DDS) tested including enzyme inhibitors, chemical modification and conventional nanoparticles have limitations like immune Reponses, extracellular vesicles (EVs), present a good solution to overcome this drawbacks. EVs have gained attention as novel delivery systems for protein and peptide therapeutics owing to their small size, biocompatibility, intrinsic targeting capabilities, lower immunogenicity, and ability to protect cargo from enzymatic degradation. EVs have demonstrated promising results in preclinical studies by enhancing the uptake, loading, penetration, and targeted release of protein/peptide cargos for conditions such as cancer, diabetes, and microbial infections. Additionally, they can serve as carriers for targeting peptides, enabling the delivery of synthetic drugs and genome-editing tools. This review explores the potential of EVs as drug delivery systems (DDS) for protein and peptide drugs, focusing on their advantages and characteristics, engineering and encapsulation, emerging EV and EV-cargo characterization techniques, release, and efficacy in overcoming the limitations of protein- and peptide-based delivery systems. The review also addresses challenges and future perspectives in translating EV-based protein and peptide delivery systems into clinical practice.