Renee Iacona on Why Future Scientists Need to Learn About Data Science
By Bio-IT World Team
December 17, 2024 | It has been a momentous year for artificial intelligence (AI) and machine learning (ML). AI models can simulate clinical trials, diagnose diseases early, and detect biomarkers, and there is still no shortage of innovative ideas. However, there are some concerns surrounding AI/ML that have also been at the forefront of the industry. For one, miscommunication between scientists, data scientists, and R&D teams is still a major issue.
Many scientists and data scientists find themselves misunderstanding each other’s work and input, resulting in delayed projects, disrupted progress, and rising frustration levels. During the Bio-IT World 2024 conference, the Data Readiness for AI and the Digital Leadership Lessons: Reflecting and Correcting panels extensively covered how R&D IT informatics, scientists, and data teams are not clearly conveying nor openly receiving their respective knowledge with each other, resulting in miscommunications, misinterpretations, and a lack of synergy. While translators can help bridge the gap, it creates extra steps for the project or initiative’s process.
Words and perspectives getting lost in translation doesn’t seem to be the primary problem, though. Perhaps the biggest obstacle is the reluctance to learn beyond one’s expertise. Renee Iacona, vice president of Oncology Biometrics, Oncology R&D at AstraZeneca, tackles this in the latest episode of Trends from the Trenches. Some people consider AI/ML buzzwords, and dismiss AI/ML’s ability to conduct research, manage data, and so on. Iacona believes that this cannot continue and that AI/ML “will play a larger role in science in the future.”
Iacona emphasizes that data, in its overabundance, needs the right skillset to be used. “What we need is scientists who can understand machine learning and AI, but more importantly, we also need machine learning and AI experts who can understand the science. Because if you can't talk to each other, then you're not ever going to get the value out of both of those.”
Thankfully, there is some proactivity to achieve this outcome. AstraZeneca has established a data science academy to help their scientists learn about data science and their data scientists learn how to speak “scientist language.” It also seems like academic institutions are taking note, as Iacona mentions that AstraZeneca hires physicians and scientists that have learned about data science in school.
“Eventually, it will be one and the same. Everyone will know what that means and will be educated in a component of it,” she adds.
Iacona is also directly partaking in educating future scientists about data science. She mentors graduate students at Vanderbilt University, where she meets them and answers questions about careers. She has encouraged them to consider understanding the world of data science so that “it will help them in the future to know the space of machine learning and AI.”
“And then, maybe, they don’t need to double down and have a double major or a double PhD,” comments Iacona. “But enough knowledge about it to already be the translator… will help them be better scientists.”
To learn more about how AstraZeneca is managing data, the focus on multimodal data, and oncology, listen to the full episode on the Trends from the Trenches podcast.