2.    Representative data

Is just having quality data sufficient, I hear you ask? Well no. We still need to ensure that the data is representative and not biased. Having certain elements of a dataset overrepresented can have serious repercussions on model performance.

3.    Data privacy

Next, you must always be aware of any privacy or accessibility issues that limit your data usage, such as personal medical data.

4.    Fine-tuning datasets

Ok, so we have a training dataset that is of high quality, is representative and not biased and has no privacy concerns. How can we use this data to train or fine-tune models? First, you must think about whether this is a collaborative effort and the security implications of such an approach; open federated learning frameworks, such as FLOWER are being developed, some work is still needed.

Furthermore, there is a whole raft of considerations that fall under the umbrella of responsible AI, that is the practice of designing, developing, and deploying AI with good intention.

Although we won’t cover all aspects of responsible AI here, some of the key points include:

6.    Subject matter expertise (SME)

This brings us to our final, but likely the most important consideration, people. Subject matter expertise (SME), particularly within the Life Sciences, is vital when configuring and running AI models. With the increased accessibility of AI, this is often the piece that gets forgotten or neglected. But how can models be fine-tuned to support a domain without an SME who understands said domain?

1.    Synthetic data generation

Understanding that the limitation on language model improvement depends on the availability of vast quantities of quality data, the ability to address this data shortage issues is of paramount importance to the domain.

To create synthetic data, it is important to have quality, representative data to build from. Being part of the Elsevier family means SciBite has the luxury of having access to a gold standard set of scientific text, coupled with our semantic technologies, puts us in a prime position to produce such datasets.

2.    Real World Data (RWD)

Application of AI to the analysis of RWD will continue to be an area of focus, whether that be EHRs, claims, patient-generated data, and data collected from other sources that can inform on patient health – such as Twitter, Reddit, and other social media platforms. Access to this data will continue to be problematic, and it remains to be seen whether synthetic data generation can truly be used to substitute for the real thing.

3.    Provenance in AI-based search solutions

As AI becomes a commodity utilized daily, we need to ensure that the provenance of decisions is captured. For example, using question-answer models to return answers, it is important to be able to see from which source, or document, the answer was extracted. This is even more important in Life Sciences, where the provenance of evidence used to support decision-making must be clear and explainable.

4.    Multimodal models

Throughout this article, we have discussed many types of AI models, image, audio, and text-based models are commonplace, but what about models that can take videos or songs as input and answer questions about them via textual input? These are known as multimodal models. Many data types are used to train these models, and as ever, SciBite is there to support the quality of textual data going into these.

SciBite and AI

At SciBite, we believe this is an extremely exciting time for AI, specifically within the Life Science space. We understand and share the excitement around the latest developments, including large language models. More than ever, we believe the foundational management of standards and application of these to data is crucial when it comes to ensuring that AI models are trained, deployed, and analyzed in as accurate a means as possible.