Human-assisted traditional peer review intelligence vs artificial peer review intelligence: perspectives on future disruptive sciences

Human-assisted Traditional Peer Review Intelligence (HATPRINT) plays a crucial role in advancing scientific discoveries through critical evaluation and expert insights by humans. However, the rise of Artificial Intelligence-assisted Peer Review Intelligence (AIPRINT), powered by Generative AI (Gen AI), including various forms of LLMs is likely to transform the peer review landscape. While Gen AI can enhance efficiency, automate reviews, and refine research, overreliance may risk losing innovation and perpetuate pseudo-peer review cycles. Several AI-assisted review systems, such as SciScore, Paperpal, and various other publishers in-house AI reviewer suggestion tools, suggest futurist dominance of AIPRINT over HATPRINT. These platforms could enhance, rather than replace, human evaluation by automating checks, improving writing, and aiding reviewer selection. Theoretically, their integration could lead to the growing convergence of HATPRINT and AIPRINT leading to a semi-autonomous peer review systems strongly underscoring the need for governance, transparency, and human oversight. We propose a balanced convergence of HATPRINT and AIPRINT to maintain scientific integrity, ensuring human expertise remains integral. However, explicit limitations are lack for empirical analysis in support of the proposed viewpoints. Future research should explore this equilibrium to sustain disruptive science while leveraging AI for enhanced accuracy, productivity, and the evolution of peer review.