AI-Powered Document Creation and Management for Life Sciences

While structured databases are designed for easy querying, and generative AI has made great strides in handling unstructured text, the life sciences faces a significant gap in terms of what data automated systems can handle. At Artos, we call this Ossified Data—data that was once structured but has been trapped in PDFs, RTFs, or other unstructured formats due to legacy systems, regulatory requirements, or internal data ownership complexities.

Regulatory submissions in life sciences are undergoing a transformation. As the volume and complexity of clinical and regulatory data grow, structured submissions have become essential for efficiency, accuracy, and compliance. The HL7 Fast Healthcare Interoperability Resources (FHIR) standard, widely adopted in clinical data exchange, is now being explored for its potential role in structured submissions under the electronic Common Technical Document (eCTD) framework.

In biotech and pharma, precision and consistency are critical. Regulatory submissions and clinical documentation demand high-quality outputs that meet stringent standards. As AI becomes essential to these processes, standardization plays a key role in ensuring efficiency and reliability. However, over-standardization can stifle productivity, while too little structure leads to inconsistency. Striking the right balance is essential for AI to thrive.