AI in Life Science: Navigating the Frontiers of Human Understanding

In a recent publication by Quantitative Biology, a series of peer-reviewed papers explores the evolving landscape of artificial intelligence (AI) in the realm of life science, shedding light on its potential to revolutionize healthcare and human comprehension. The papers delve into the rapid developments of AI, emphasizing its impact on science, technology, and social relationships.

Michael Q. Zhang, the Cecil H. and Ida Green Distinguished Chair of Systems Biology Science, and professor of biological sciences at the University of Texas, Dallas, highlights the transformative potential of machine learning and AI. Zhang, also co-editor-in-chief of Quantitative Biology, underscores the journal’s role as a platform for stimulating intellectual discourse on AI’s role in life science.

One notable editorial, “Dialog between artificial intelligence & natural intelligence,” authored by Zhang, imagines a conversation between AI and natural intelligence (NI), framing their fundamental purposes and ultimate objectives. While NI prioritizes population survival, AI aims to extend and maximize human capabilities, positioning itself as a complement to the human brain.

Xuegong Zhang, professor of automation at Tsinghua University and executive editor-in-chief of Quantitative Biology, introduces the concept of Digital Life Systems (dLife) in a perspective study titled “Building digital life systems for future biology and medicine.” The proposed dLife paradigm seeks to comprehensively integrate AI for digital investigation, digitally twinning full systems, including individual human bodies, to provide quicker and more accurate insights into potential treatment benefits or side effects.

Gangqing Hu, assistant professor of microbiology, immunology, and cell biology at West Virginia University, presents the OPTIMAL model in a prospective study, “Empowering beginners in bioinformatics with ChatGPT.” This innovative model employs iterative mentoring and assessment with an LLM (large language model) chatbot to enhance students’ coding skills and critical creative thinking in bioinformatics data analysis.

In his commentary, “Simulating the whole brain as an alternative way to achieve AGI,” Jianfeng Feng, dean of the Institute of Science and Technology for Brain-Inspired Intelligence at Fudan University, challenges the notion that ChatGPT represents artificial general intelligence (AGI). Feng advocates for a deeper understanding of the dynamic operation of the human brain and proposes simulating the entire human brain with 86 billion neurons simultaneously to achieve AGI.

These articles collectively advocate for a human-AI integrative approach to advance both AI capabilities and human understanding simultaneously, positioning it as the optimal path for AI in life science.