Generative AI (Artificial Intelligence) comes with a promise of offering unparalleled opportunities to life sciences organizations. Yet, the success of the journey grips on how data ready is your company for gen AI.
From improving drug discovery to enhancing trials and devising marketing strategies, there is a vast potential to take benefit from the use of gen AI applications. However, the hurdle most Chief Data Officers (CDOs) and data leaders in the life sciences domain are facing is managing data and scaling AI use cases. Now, they need to focus on making changes within the data and the architecture for gen AI to produce meaningful results for the business.
In this blog, we explore the importance of making data ready for generative AI and actionable insights for life science companies to navigate the generative AI data with confidence.
Data quality affects the accuracy, dependability, and consistency of algorithmic patterns and results of gen AI applications. To ensure its standards, organizations should build strategies comprising of data validation and data cleansing methods.
Data validation refers to authenticating the accuracy of information through different facets. It includes verifying the data for errors, patterns, and inconsistencies and ensuring it runs parallel to the organizations’ standards. While the data cleansing process is implemented to fix the errors found during validation, it involves eliminating duplication, correcting errors, and standardizing the data for overall consistency.
Data validation is decisive for Gen AI applications as it makes sure the data presented to AI models is reliable, consistent, and precise. Without validation, the input data could have inconsistencies, biases, and errors, leading to variable and unreliable AI-led output.
These make sure that AI models are trained to offer reliable and high-quality data for organizations to lay their problem-solving decisions.
Data readiness for gen AI involves multi-layered tactics with a few components that are critical for organizations.
Next, let us look at the steps involved in preparing data for gen AI usage.
To leverage the power of Gen AI, the data should be prepared well. Here are the four critical steps to prepare life sciences data for gen AI.
The fundamental practice of preparing data for gen AI starts with acquiring data from diverse datasets and curating relevant data. The data should consist of all the critical components which are essential to generate the right response.
For example, while acquiring data for drug development care should be taken to include chemical structures, target proteins, biological assays, drug reactions, and trials. Data can be obtained from academic literature, internal records, public repositories, and proprietary databases.
The next focus should be on the creation of data by cleaning the acquired data and standardizing it to maintain quality and consistency. At the same time, the steps should involve correcting errors, eliminating duplication, and regulating data formats. Additional data including patient demographics, assay conditions, and molecule identifiers should also be analyzed and cleared for further data interpretation and training models.
This step involves improving the available data, particularly when it is disorganized or limited. For generating superior results from gen AI cleansing and preprocessing methods must be applied.
Data synthesis is the method implemented, which involves creating new data samples based on the available data. A few generative AI techniques at this stage include interpolation and extrapolation, which means creating synthetic data as per the statistic models. Data synthesis is a broad concept that constitutes methods to create new data and is not limited to merely resampling.
Gen AI models like generative adversarial networks and variational autoencoders can synthesize data samples from the curated data. Nonetheless, it should be ensured that the data reflects the real-world annotations.
This is a critical stage as the data collected must go through sifting, where the raw data transforms into a standard format appropriate for training gen AI models and contribute to visionary performance. For example, the data for drug development should undergo changing biological sequences for numerical embeddings, encode chemical structures, and extract information from clinical data. Some of the techniques involved at this stage are normalization, dimension reduction, and selection for computational efficiency.
Life sciences data for gen AI should be validated and facilitated for model training. This step involves adhering to quality based on accuracy and reliability for AI models. Conducting experiments, validating datasets, and checking for model robustness are a few more steps to assess the performance of gen AI models. The approach begins with a base model and then passes through layers of SFT (Supervised Dine Tuning), RLHF (Reinforcement Learning from Human Feedback), and Proximal Policy Optimizations. Another crucial aspect of model building is moderation, which helps to generate relevant data by eliminating socially irresponsible answers.
Finally, Subject Matter Experts (SMEs) are required to verify the final data samples and ensure it aligns with drug discovery and biological plausibility. Adding a human element is necessary to validate the gen AI responses and test the data quality. Some other measures like implementing control mechanisms and data governance are critical to maintain reliability and integrity.
In the era of AI-driven world, the potential of data readiness in leveraging pharma organizations should not be overlooked. From enhancing drug discovery processes to clinical trials, and coming up with unparalleled marketing strategies, gen AI applications have the potential to energize the pharma organizations. Adhering to meticulous data preparation through advanced practices and accelerating pharma organizations to use gen AI’s full potential and result in breakthroughs and innovation in the healthcare and drug development industry.
The future is gen AI and i2e Consulting can help you prepare for it. Our data scientists are experts in preparing data for gen AI models. We can also advise on implementing control mechanisms and data governance practices.
