Applications of Artificial Intelligence in Biotechnology: Current Trends and Future Prospects

Artificial intelligence (AI) is becoming an important tool in biotechnology research because it can analyse large and complex biological datasets more quickly than traditional approaches. Applications include drug discovery, genomics, proteinstructure prediction, disease diagnosis, personalised medicine, and biomanufacturing. This paper reviews the main uses of AI in biotechnology and examines its possible benefits, including faster research, improved prediction, automation, and reduced development costs. It also discusses limitations such as poor-quality data, algorithmic bias, lack of explainability, privacy risks, and difficulties in validating AI-generated results. The review argues that AI should be treated as a decision-support technology rather than a replacement for biological expertise. Responsible adoption requires high-quality datasets, human oversight, transparent methods, secure data governance, and appropriate regulation. The paper concludes that AI is neither completely good nor completely bad in biotechnology. Its value depends on how it is designed, tested, and used. When combined with laboratory validation and ethical safeguards, AI can strengthen biotechnology research and support the development of safer and more effective solutions.