AI models thrive on structured, high-quality data. However, biomedical research presents unique challenges that make AI integration particularly difficult.

1. Biomedical Data Is Messy and Heterogeneous

Unlike standardized datasets in finance or natural language processing, biomedical data comes in many forms: microscopy images, genomic sequences, patient records, and multi-modal datasets from different instruments. There is no single standard governing how these datasets should be stored, labeled, or structured. According to Han (2025), one of the biggest obstacles to reproducible AI in biomedical research is the inherent conflict between standardization and researchers’ individualized data handling practices. As a result, simply acquiring a dataset is not enough—it needs extensive preprocessing, filtering, and standardization before AI can make sense of it.

2. Scientists Are Not Data Scientists

Many biomedical researchers are highly skilled in their respective domains but lack formal training in data science or machine learning. A common problem in academic and biotech labs is suboptimal data storage and organization—files are often stored in fragmented formats, with inconsistent metadata and minimal documentation (Wilkinson et al., 2016).

This gap leads to poor reproducibility and makes datasets difficult to integrate into AI workflows. As AI adoption grows, the need for better data curation strategies and training for scientists in data handling is becoming increasingly evident.

3. Annotation is Expensive, Time-Consuming, and Requires Expertise

Data annotation is a well-documented bottleneck in AI training. For biomedical datasets, annotations often require domain-specific knowledge—a task that cannot be outsourced to non-experts without compromising accuracy. In medical imaging, for instance, expert-labeled data is essential for training diagnostic models, but the annotation process is slow and resource-intensive (Litjens et al., 2017).

The challenge extends beyond simple labeling. Variability in annotations due to subjective interpretation introduces bias into AI models, making it difficult to achieve reliable outcomes across different datasets.