Bridging Biology and Artificial Intelligence: Opportunities, Tools, Challenges, and Ethical Considerations

Abstract

Artificial intelligence (AI) is now widely used in biological research. It helps researchers analyse large datasets, identify patterns, and make predictions that are difficult to obtain by manual analysis alone. In genomics, AI can relate DNA sequence to gene regulation and variant effects. In structural biology, deep-learning systems such as AlphaFold have greatly improved protein-structure prediction. AI is also being used in drug discovery, medical data analysis, ecology, and biodiversity monitoring. These advances are important, but they do not remove the need for biological reasoning or experimental validation. Model performance depends strongly on the quality and diversity of training data, while many high-performing models remain difficult to interpret. The use of genomic and clinical data also raises concerns about privacy, fairness, accountability, and unequal access to computational resources. This review examines major applications of AI in biology, summarizes the main computational approaches, and compares their strengths and limitations. It also considers ethical issues and identifies areas where human judgement remains essential. AI is most useful when it supports, rather than replaces, biological expertise, careful experimental design, and transparent scientific decision-making.