The Artos Blog

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.

As enterprises increasingly explore AI, tools like ChatGPT provide accessible, flexible options for generating text and running basic functions like table creation or visualization. And, at Artos, we absolutely love tools like ChatGPT, Claude, and Perplexity.

In the rapidly evolving landscape of generative AI, curiosity and hands-on experimentation are some of the most powerful tools we have to unlock its potential. At Artos, we’ve seen how experimentation not only builds an intuitive understanding of what AI can achieve but also reveals the often-underestimated complexities involved in building robust, scalable AI solutions. Here, we’ll dive into why experimentation with generative AI is vital, from uncovering AI’s unique strengths and limitations to understanding the true challenges of scaling these technologies effectively.

At Artos, we believe that AI is uniquely suited to transform the field of medical and regulatory writing—and to make its experts even more essential in the process. AI’s capacity to streamline repetitive, detail-intensive tasks offers a chance for medical writers to shift their focus from low-level work to high-level, strategic contributions that can drive real value for life sciences companies.

In life sciences, creating documents for regulatory submissions is a crucial, complex, and highly regulated task. AI has increasingly become a tool of choice for automating and enhancing these processes, from document generation to classification and even data extraction from scientific studies. But not all AI models are created equal, and their impact on the performance of enterprise AI applications can vary greatly.

In the life sciences sector, regulatory submissions like INDs (Investigational New Drug applications) and NDAs (New Drug Applications) are complex, time-intensive, and incredibly nuanced. These documents must meet rigorous regulatory requirements that differ significantly depending on the product type—biologics versus small molecules, for instance. The challenge is twofold: not only must submissions be technically accurate and comprehensive, but they must also follow the specific formats and structures expected by regulatory authorities.