Next year promises some exciting developments with artificial intelligence and big data within the health technology space. By health technology this covers a vast array within medicines, medical devices, vaccines, procedures and health provision systems, each developed to solve a particular health problem.
To understand how these developments will become integrated, Digital Journal heard from Andrew Kasarskis, Chief Data Officer at Sema4.
Andrew Kasarskis predicts that innovations in health technology will help to improve medical diagnostics, leading to improved patient outcomes. Kasarskis states: “We continue to see great advances in machine learning (ML) and artificial intelligence (AI) applied to large information-rich data sources in fields such as image analysis and natural language processing, and I don’t expect that will slow down at all.”
As an example, Kasarskis highlights: “Some of these algorithms are already being successfully applied to biomedical data and are great at grouping and classifying data and entities represented by vectors, matrices, or cubes of data.”
Kasarskis picks two important data technology trends that he thinks will be important in 2022, seen through the lens of biomedical data types and health intelligence needs.
Efficient allocation of data curation resources
Data curation refers to the activities around the organization and integration of data, where the data has been collected from various sources. The process involves annotation, publication and presentation of the data, with the intent that the value of the data is maintained over time.
With data curation, Kasarskis explains: “This is a need for technological and process innovation that I’d love to exist but don’t yet see happening. When obtaining those large corpuses of well-labeled data to train the AI, some human manual and semi-manual work is inevitably needed.”
Kasarskis outlines why development has been uneven up until now: “This work is always expensive, never scales well, and frequently takes experts with esoteric knowledge away from important value-generating activities. Figuring out the most efficient way to allocate manual curation work seems, to me, like a significant unmet need that impedes progress in the use of data technology, particularly in biomedicine.”
Continued focus on data equity
AI is only as good as the base programming and it biases exist at the outset; they will continue. The concept of data equity is about appreciating we are all making choices that reflect a particular worldview.
As Kasarskis explains: “Societal biases and inequities can be present whenever data is used. I expect individuals and organizations to continue discovering errors, omissions, and blunders in their data where biases in collection and storage led to incorrect, misleading, and harmful outcomes.”
For this form of healthcare technology to develop, Kasarskis recommends: “Continued focus on identifying and resolving these issues is important for both accuracy of conclusions and equity in data use.”
