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Artificial Intelligence: Success Criteria in Life Sciences

Previously, I dove into how artificial intelligence helps review, and provide statistical analysis to data. The final blog of this series outlines how to be successful with an artificial intelligence implementation.

Setting initial expectations and not promising a magic bullet is a key factor in determining the success of an initiative that focuses on deploying AI to streamline the clinical data review and cleaning process.

Training the machine learning model will determine how accurate the results are. After every phase, evaluating the released functionality, reassigning priorities to backlogged initiatives, and releasing based on prioritized functionalities should be done and closely monitored. The adoption by business users will determine how successful these initiatives are. Other factors that will help in evaluating how success include:

  • Accuracy and speed of data review
  • Effort needed by humans to reach a milestone
  • Improved user experience
  • Adoption of the solution by the end-user community

Extending the Roadmap

AI technologies can be used for more initiatives within CDRP. Each of the questions that need to be answered in the initiatives could be addressed by ML models based on how well the models are trained. In addition to CDRP, there are other areas in life sciences – such as safety systems, clinical operations systems, risk-based monitoring systems, and data capture systems – where AI technologies can be used to enrich the data management process.

To learn more about how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process, you can download the guide here or submit the form below.

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