Here’s how biotech professionals say AI is changing the field

What could associate professors, doctors and CEOs have to discuss? 

If you attended Women in Bio’s annual Pittsburgh Outstanding Women Entrepreneurs Rally (P.O.W.E.R.) at Carnegie Mellon University on Thursday night, you could’ve heard them discuss how AI is impacting biotech and their work. But, if you couldn’t make it in person, never fear: Here are a few of the ways women in biotech said AI is creating challenges and opportunities in their field.

It’s providing more data

From in-house collection to crowdsourcing, there are many ways AI can be used for data processes. For CMU Computational Biology Department Assistant Professor Oana Carja, that’s an exciting thing. Carja reasoned that technologists currently use machine learning and AI to get better answers, and she’s had the opportunity to collaborate with algorithms and computers to help her ask better questions. For her, AI has the potential to help scientists become better at making sense of data.

“I think the hardest part about being a scientist is figuring out what are good questions to ask,” Carja said. “I think we’re slowly moving in that direction. Using automated learning and having AI and machine learning can help us design this knowledge [and] process the data at the end of the experiment.”

It can improve patient care

As an OBGYN and the CEO and founder of de-bi, co (formerly known as Heny Inc. when it first appeared on the 2023 Pittsburgh RealLIST Startups list), Marielle Gross is most interested in how AI can improve patients’ experiences and health outcomes. Gross is excited that AI-driven tools have enabled adding custom design elements to 3D models without compromising the fabricated objects’ functionality, thus making them more accessible to clinicians. This matters because 3D culture models are often used to study cancer development and drug response. More functional and accurate models better enable doctors to develop treatments that benefit their patients, she said.

“[What] I’m focused on first is cancer treatments,” Gross said. “So, it’s really the idea of an entity that’s combined with [the] richest data that we have in a physical solid state, with the power of AI to really do experiments that can inform directly patient care, in real-time, in a way that I believe can really be disruptive for many classes of disease.”

It’s speeding up the drug discovery process

Coming from the world of life sciences, NVIDIA ecosystem business development leader Renee Yao is excited that generative AI has been used in the earliest discovery and drug discovery pipeline. This means that AI tools can assist researchers with drug discovery, design and predictions about the effectiveness of drugs. Yao said AI tools are helping facilitate the drug discovery process.

“A lot of those are making huge headway to supplement the drug discovery process,” Yai said. “And we see these overhauls are important in many of the different types of domain-specific large language models used to perform tasks to help accelerate this market transition to the next phase of potentially an insurer AI design drugs.”

It can help prevent diseases and better diagnose practices

As someone running a company whose AI-fueled platform uses genetic and clinical data to create insights for early diagnosis, Ariel Precision Medicine CEO and cofounder Jessica Gibson is most excited about the way AI can impact disease prevention and diagnosis for patients. Gibson said AI imaging can identify problems that might not be immediately visible to the human eye. Examples include analyzing data to distinguish between something benign and a not-readily-apparent abnormality, or, in some cases, detecting injuries not initially detectable on an X-ray.

“We’re at such an exciting time, with the emergence, I think, of more mainstream [tools] like ChatGPT [which] kind of brought the rest of the public into recognizing the power and the potential of it,” Gibson said. “Imaging is probably one of those cases that’s leading to pattern recognition. What I’m excited to see [is] some of the work that’s been done in oncology translated into actual clinical practice.”

How do you think AI impacts biotech? What are some of the biotech and AI intersections you’d like to see us take a close look at? As always, we’d love to hear your thoughts: pittsburgh@technical.ly.