The application of artificial intelligence in research protocol development: A critical analysis of benefits, limitations, and political-economic contexts

Abstract

Research protocol development represents a fundamental cornerstone of scientific inquiry, yet traditional approaches relying on domain expertise and manual literature reviews are increasingly inadequate for addressing contemporary research complexity and exponential scientific data growth. The integration of artificial intelligence into this process occurs within contested political, economic, and epistemological contexts that shape what research becomes possible or prioritized. This narrative review systematically examines AI technologies applicable to protocol development, analyses real-world case studies across diverse research domains, identifies benefits and limitations of AI integration, critically evaluates political-economic contexts shaping AI deployment, and proposes recommendations acknowledging power asymmetries and epistemological constraints. A comprehensive literature search was conducted across PubMed, IEEE Xplore, ACM Digital Library, Web of Science, and Google Scholar (2015-2025). Inclusion criteria encompassed peer-reviewed articles, conference proceedings, technical reports, and case studies addressing AI applications in research protocol development. AI applications suggest emerging potential across natural language processing for literature synthesis, machine learning for study design optimisation, and automated ethical compliance checking. However, challenges persist including data quality issues, algorithmic transparency concerns, corporate control of AI systems, political restrictions on certain research domains, and systematic bias toward methodologically conservative designs that may disadvantage novel, theoretically complex, or population-heterogeneous research. Critical examination reveals AI optimization encodes particular values about what constitutes "good science," potentially foreclosing paradigm-challenging research and methodologically innovative approaches. Independent peer-reviewed validation remains limited for many claimed benefits, with substantial evidence deriving from corporate sources rather than independent evaluation. AI offers reported potential for research protocol development. However, realization occurs within power structures shaped by corporate ownership, political constraints, and epistemological biases. Future success requires collaborative human-AI frameworks maintaining critical awareness of how efficiency metrics may systematically disadvantage certain knowledge production forms, supported by independent evaluation rather than corporate claims. • AI technologies reduce protocol development time in major initiatives • NLP systems automate literature synthesis for comprehensive protocol reviews • Machine learning optimises study design through predictive modelling • AI-assisted protocols show improved efficiency across biomedical research • Human-AI collaboration essential for maintaining research quality and ethics

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