The integration of artificial intelligence (AI) in healthcare is accelerating at a tremendous pace. However, the efficient utilization of AI in healthcare largely depends on the quality of the data. According to studies, 80% of healthcare data exists in unstructured formats, making it challenging for AI algorithms or large language models to extract meaningful insights.
The phrase "garbage in, garbage out" aptly describes this situation. To truly harness the capabilities of generative AI in healthcare, it's essential to address and overcome the challenges related to data quality and to maintain clean data.
Challenges with medical data quality
Moreover, medical or lab data usually contains inaccuracies, incomplete information, and lacks validity. These data quality issues can mislead the AI models into perceiving patterns that don't actually exist, which can further lead to inaccurate or misleading results. Therefore, it's crucial to understand and address these pitfalls while preparing data for machine learning models.
Because of these challenges, it's crucial for healthcare organizations to put in place certain tools or processes for assessing, cleaning, and standardizing their data before utilizing it for AI technologies.Clinical terminology tools that codify clinical notes to industry standards can help improve the data quality going into AI models.
The risks of poor quality data in AI training
The successful integration of AI in healthcare largely depends on the quality of the data. Training AI models with unclean or messy data can lead to several complications such as a decrease in accuracy and the inclusion of bias. Insufficient or overly simplified data related to minority populations can cause bias to be built into the model, which may lead to wrong assumptions and poor recommendations.