Emerging Artificial Intelligence Trends in Life Science and Biotechnology

This research looks at how advanced AI methods can be used in life sciences and biotechnology, especially for handling large‑scale biological data. Rapid growth in genomic sequencing, high‑resolution digital pathology, and electronic health records has created complex, mixed‑type data that needs strong computational models. The study evaluates several AI tools: – Deep‑learning frameworks – Generative designs for molecules – Cutting‑edge protein‑structure predictors such as AlphaFold It also examines how intelligent systems can improve epidemiological forecasts and precision‑medicine applications. To overcome key challenges—model interpretability, data heterogeneity, and ethical oversight—the work proposes a clear, step‑by‑step method for building transparent and fair AI pipelines. By setting strict benchmarking rules for generative drug‑discovery and

synthetic‑biology workflows, the framework helps users: – Test model performance rigorously – Meet regulatory requirements – Keep computational costs low Through systematic reviews and simulation guidance, the research supports the creation of explainable AI models that can navigate complex biological systems.

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