Research from the University of North Carolina is exploring how AI and robotics can automate laboratory processes, enhancing safety and operational efficiency.

For instance, Jennifer Kang-Mieler is developing an AI tool designed to diagnose retinopathy of prematurity in infants, showcasing AI's potential to expedite traditionally manual diagnostic processes.

In light of these funding reductions, there is growing interest in leveraging artificial intelligence (AI) to support ongoing research efforts.

Significant budget cuts to federal agencies, including the National Institutes of Health and the U.S. Centers for Disease Control, have prompted a search for innovative solutions to sustain scientific research.

Stephen Wong from Houston Methodist employs AI for tasks like image analysis and processing large datasets, allowing researchers to concentrate on more complex challenges.

While there are concerns about job displacement due to automation, experts argue that this shift could create new opportunities in data science and AI specialization.

AI is increasingly being utilized for various applications, including data analysis, laboratory efficiency, diagnostics, and personalized treatment plans, although its implementation is still in early stages.

In February 2025, the U.S. Department of Energy collaborated with AI companies OpenAI and Anthropic for an event where 1,000 scientists explored the applications of AI in research.

AI's contributions to scientific advancement are already notable, as demonstrated by the 2024 Nobel Prize in Chemistry awarded for AI-assisted protein discoveries.

Experts, including Bradley Bostic, emphasize the importance of human oversight in AI applications, advocating for a collaborative approach where AI complements rather than replaces human expertise.

This sentiment is echoed by Kang-Mieler, who cautions that while AI can enhance decision-making, it cannot fully replace the roles of human researchers or physicians.

However, the effectiveness of AI is contingent upon the quality of training data, with risks of bias and incomplete information potentially leading to significant errors in research and healthcare.