There is increasing concern that AI-generated images and data could pose a threat to scientific research. While AI technology is enhancing efficiency in the biohealth sector by automating image-based diagnostics and research analysis, there are risks that AI-generated images or data could lead to false information or biased conclusions. Particularly in biohealth research, confusing AI-generated images with real data or misinterpreting them could seriously compromise the reliability of actual research findings.

As AI-generated images become increasingly indistinguishable from real ones, researchers emphasize the need for technologies to identify and verify them. It is essential to develop algorithms or verification systems that can detect AI-generated images and implement systems that clearly document the source and generation process of research data. Such technologies can prevent AI-generated images from infiltrating the research process and help maintain the reliability of research results.

However, a major challenge lies in the fact that technologies for identifying AI-generated images are not yet perfect. Particularly with the emergence of AI models capable of generating complex biological images, there are clear technical limitations in detection as AI-generated data becomes increasingly similar to real data. This suggests the need for new approaches to research data quality control and verification.

Ultimately, minimizing the potential negative impact of AI-generated images on research requires strengthened regulations and policies. For example, guidelines should be established for transparently disclosing the generation process of image data used in research papers and strictly distinguishing AI-generated data. For AI-based biohealth innovation to yield meaningful results, it is essential to have technical and policy measures that can recognize and address the risks and limitations of AI-generated images.

Therefore, to ensure that the convergence of AI and genomic data in the biohealth field leads to meaningful innovation, efforts must be made to simultaneously strengthen the detection and verification of AI-generated images while protecting the reliability of scientific research and data.