AI in life sciences

How Artificial Intelligence is shaping life sciences; Systematic qualitative review with a future-oriented analytical framework Running title: Future trends and emerging directions of AI in life sciences Short title: AI Future Directions in Life Sciences Ambreen Ilyas, PHD, School of biological Sciences, University of the Punjab, Lahore Abstract Artificial intelligence (AI) is rapidly evolving from a supportive analytical tool into a central driver of future innovation in the life sciences. As biological research enters an era defined by large-scale, high-dimensional, and continuously generated data, AI is increasingly positioned to shape how biological knowledge is discovered, validated, and translated into real-world applications. This future-oriented review synthesizes emerging trends in AI-driven life-science research, emphasizing the transition toward digitally integrated, data-centric, and adaptive research ecosystems. Current evidence indicates that machine learning and deep learning approaches will play a pivotal role in redefining experimental design, predictive modeling, and decision-making across genomics, drug discovery, precision medicine, agriculture, and environmental biology. Looking forward, AI is expected to enable seamless integration across biological scales from molecular interactions to ecosystem dynamics through intelligent data fusion, automated hypothesis generation, and real-time learning systems. These advances are likely to accelerate discovery while supporting sustainable and resilient biological innovation. Despite its transformative potential, the future deployment of AI in life sciences is constrained by challenges related to data quality, interpretability, ethical governance, and system interoperability. Emerging trends such as explainable artificial intelligence, hybrid data–knowledge models, digital twins, and responsible AI frameworks are increasingly recognized as essential for building trust, reproducibility, and regulatory acceptance. This review highlights key technological, methodological, and conceptual directions that are expected to define the next generation of AI-enabled life sciences. This review combines systematic evidence mapping with a future-oriented analytical framework to guide responsible and biologically aligned AI innovation in the life sciences. By positioning AI as a collaborative and adaptive scientific partner rather than a purely computational instrument, future research can better align artificial intelligence with biological understanding, societal needs, and long-term sustainability goals. Keywords: Artificial intelligence; life sciences; future trends; digital biology; machine learning; explainable AI; sustainability