A workshop focusing on deep learning and machine learning will be held on April 8 and 9 at the Downtown Library Complex, Room 104. This event aims to equip participants with essential skills in a rapidly evolving field.
Recent statistics reveal that the global average cost of Phase 3 development programs has now surpassed $1.2 billion. This significant investment underscores the increasing reliance on advanced technologies, including deep learning, within the pharmaceutical industry.
However, a concerning trend has emerged: fewer than 12% of surveyed pharmaceutical organizations reported having formal drift detection mechanisms for their production clinical AI models. This gap raises questions about the reliability of AI systems over time.
The average period between deployment and database lock for these Phase 3 programs is approximately 28 months, indicating a lengthy process that could impact the timely application of AI advancements in clinical settings.
Organizations that have implemented feature stores report a median 43% reduction in duplicated feature engineering efforts across model teams. This efficiency gain highlights the potential of deep learning to streamline processes in data management.
The FDA has proposed a Predetermined Change Control Plan framework, envisioning pre-approved protocols for updating AI models in production. This initiative aims to ensure that AI systems remain effective and compliant as they evolve.
MLOps, which applies DevOps principles to AI, emphasizes the need for robust infrastructure to deploy, update, and monitor AI models effectively. The maturation of MLOps infrastructure is crucial for the productive deployment of AI in clinical data operations.
As the pharmaceutical industry continues to invest in machine learning applications, including query prediction and anomaly detection, the potential value of clinical AI remains high. However, experts warn that without continuous monitoring and drift detection, models may degrade invisibly.
“The result is a widening gap between the potential value of clinical AI and its realized operational contribution,” one expert noted, emphasizing the importance of addressing these challenges.
As organizations prepare for the upcoming workshop, the critical question remains: will the AI deployed today continue to function accurately and reliably two years after its initial deployment?