AI is enabling historians to analyze vast amounts of historical documents more efficiently than traditional methods. By using machine learning and deep neural networks, researchers can uncover patterns and connections across large datasets, which helps to correct distortions that arise from examining history one document at a time. For instance, projects like the Venice Time Machine aim to digitize extensive historical records, allowing for a more comprehensive understanding of social networks and historical contexts.

Historians face several challenges when integrating AI into their research. One major issue is the potential for bias and inaccuracies in machine learning models, which can lead to distorted interpretations of historical data. Additionally, AI models often struggle with poorly handwritten texts or regionally specific languages, limiting their effectiveness. There is also a concern that AI-generated content could introduce false records or misrepresent historical figures, which complicates the task of maintaining accurate historical narratives.

Machine learning enhances the study of historical documents by allowing researchers to classify, cluster, and analyze large volumes of data that would be unmanageable through manual methods. For example, neural networks can identify and categorize illustrations and tables from historical texts, revealing insights about the evolution of knowledge over time. This capability enables historians to draw broader conclusions from limited samples, potentially reshaping our understanding of historical events and trends.