Advancing Biology and Medicine through Systems Thinking and Artificial Intelligence / Machine Learning Technologies

The integration of systems thinking into science has its roots in early 20th-century theories, but its application in conjunction with machine intelligence technologies like artificial intelligence/machine learning in the domains of biology and medicine is still emerging.

Historically, the focus on specific technologies such as machine learning (ML), Artificial Intelligence (AI), robotics, blockchain, and encryption has progressed in isolation, showing significant advancements independently. Yet, their incorporation into broader scientific frameworks, specifically within systems biology and systems medicine, faces ongoing challenges. These fields have begun to adopt a more integrated approach, utilizing cutting-edge technologies to foster a comprehensive understanding of biological and medical sciences. Nevertheless, the broader application of systems thinking to effectively harmonize these technologies within the scientific community remains limited.

This Research Topic aims to explore and emphasize the use and impact of systems thinking in the integration of advanced technologies like AI/ML within the fields of biology and medicine. The goal is to advance a holistic approach that does not merely focus on individual technological solutions but rather on how these technologies can be integrated into existing scientific systems to enhance understanding and efficiency in biological and medical practices.

To gather further insights into this integrative approach, we welcome articles addressing, but not limited to, the following themes:
o Conceptual frameworks that integrate systems thinking with AI/ML
o Applications of AI/ML using a systems approach
o AI usage in precision medicine and personalized healthcare
o Brain Computer Interfaces
o Policy development for the regulation and promotion of integrated technological systems
o Case studies on the effective fusion of technology and systems in real-world medical and biological settings
o Systems thinking applied to robotics
o AI for disease prediction and diagnosis
o Reviews on the state of systems thinking in current medical and biological research
o Future directions and innovations at the intersection of systems biology, systems medicine, and technology
o Ethical and societal impacts of AI in Biology and/or Medicine

The webinar entitled "Building Systems Thinking" with Dr. Leroy Hood is an additional resource available for those interested in further exploring this topic HERE.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Systems Thinking, Systems Biology, Systems Medicine, Machine Learning, LLMs, AI, Artificial Intelligence, Precision Medicine, Healthcare, Integrative, Robotics, Brain-Computer Interfaces, Policy, Ethics, Frameworks

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.