American Journal Of Biomedical Science & Pharmaceutical Innovation

Artificial Intelligence–Driven Transformation Of Medical Education: Pedagogical Innovations, Ethical Challenges, And Future Directions

Rafael M. Ortega — 2026-01-01

Artificial intelligence has emerged as a transformative force across healthcare systems, with medical education representing one of its most consequential domains of influence. The rapid integration of artificial intelligence–based tools into medical curricula has altered how knowledge is delivered, acquired, assessed, and retained, while simultaneously reshaping the roles of educators and learners. This article presents an extensive, theory-driven examination of artificial intelligence in medical education, grounded strictly in established scholarly literature. Drawing upon contemporary research, the study explores current applications such as adaptive learning systems, machine learning–assisted assessment, intelligent tutoring, virtual patients, and simulation-based environments. Particular emphasis is placed on how these technologies address long-standing pedagogical challenges, including variability in learner preparedness, limitations of traditional assessment models, and constraints imposed by clinical training environments. The article also situates artificial intelligence within the broader disruption caused by the COVID-19 pandemic, examining how global educational crises accelerated digital and intelligent innovation. Ethical considerations are explored in depth, including algorithmic bias, data governance, transparency, accountability, and the evolving professional identity of future physicians. Through a descriptive and interpretive methodological approach, this work synthesizes existing evidence to identify both opportunities and risks associated with artificial intelligence–driven education. The findings suggest that while artificial intelligence holds significant promise for personalizing learning and improving educational outcomes, its successful and responsible implementation depends on robust ethical frameworks, faculty development, and institutional governance. The discussion highlights theoretical implications for medical pedagogy, outlines limitations in current research, and proposes future directions for scholarly inquiry and policy development. Ultimately, the article argues that artificial intelligence should be viewed not as a replacement for human educators, but as a powerful augmentation capable of reimagining medical education in alignment with societal needs and professional values.

Development Of An Effective Technology For Extraction Of Cholegeric Collection From Local Medicinal Plant Raw Materials

M.Zh.Khudoiberdieva — 2026-01-08

This paper presents the development of a technology for the aqueous-alcoholic extraction of biologically active substances from a multi-component choleretic herbal collection consisting of calendula officinalis (Calendula officinalis), flowers of common tansy ( Tanacetum vulgare ) and peppermint leaves ( Mentha piperita). The following quantitative indicators were chosen as criteria for the process efficiency: the sum of flavonoids (calculated as rutin) and the content of extractive substances.

A series of laboratory experiments was conducted with varying parameters: ethanol concentration (20–70%), temperature (20–80 ° C), stirring speed (40–160 rpm), and extraction time (15–90 min). Extraction efficiency was found to be most significantly influenced by extractant concentration and temperature. Optimal values for these factors were determined. The maximum yield of total flavonoids and extractive substances was achieved under these conditions, amounting to approximately 92.3% for total flavonoids and 91.7% for extractive substances of the maximum possible values. The obtained results confirm the scientific validity and practical applicability of the proposed technology.

The Effect Of Planting Dates And Seedling Density On The Development Of Sugar Beet Across Growth Stages

Choriyev Erali Olimovich — 2026-01-14

In this article, under the conditions of the typical sierozem soils of the Tashkent region, when the sugar-beet varieties “Sado” and “Eldona” were sown between March 20–30 and April 5–15, it was observed that the variety “Eldona” is 4–5 days later-maturing compared to “Sado.” The growing period of the varieties, when sown between March 20–30, amounted to 179–182 days, and when sown between April 5–15, reached 183–186 days. Furthermore, the study reports that when the varieties were sown on March 20–30, their growing period ranged from 175 to 182 days, whereas sowing between April 5–15 resulted in a growing period of 179 to 186 days.