Biomedical engineers at Georgia Tech created a treatment that could one day unlock a universal strategy for treating some of the hardest-to-treat cancers — like those in the brain, breast, and colon — by teaching the immune system to see what it usually misses.

Their experimental approach worked against those kinds of cancers in lab tests and didn’t damage healthy tissues. Importantly, it also stopped cancer from returning.

While the therapy is still in early stages of development, it builds on well established, safe technologies, giving the treatment a clearer, quicker path to clinical trials and patient care.

Reported in May in the journal Nature Cancer, their technique is a one-two punch that flags tumor cells so they can be recognized and then eliminated by specially enhanced T cells from the patient’s own immune system.

Get all the details on the College of Engineering website.

Asking artificial intelligence for advice can be tempting. Powered by large language models (LLMs), AI chatbots are available 24/7, are often free to use, and draw on troves of data to answer questions. Now, people with mental health conditions are asking AI for advice when experiencing potential side effects of psychiatric medicines — a decidedly higher-risk situation than asking it to summarize a report. 

One question puzzling the AI research community is how AI performs when asked about mental health emergencies. Globally, including in the U.S., there is a significant gap in mental health treatment, with many individuals having limited to no access to mental healthcare. It’s no surprise that people have started turning to AI chatbots with urgent health-related questions.

Now, researchers at the Georgia Institute of Technology have developed a new framework to evaluate how well AI chatbots can detect potential adverse drug reactions in chat conversations, and how closely their advice aligns with human experts. The study was led by Munmun De Choudhury, J.Z. Liang Associate Professor in the School of Interactive Computing, and Mohit Chandra, a third-year computer science Ph.D. student. De Choudhury is also a faculty member in the Georgia Tech Institute for People and Technology.

“People use AI chatbots for anything and everything,” said Chandra, the study’s first author. “When people have limited access to healthcare providers, they are increasingly likely to turn to AI agents to make sense of what’s happening to them and what they can do to address their problem. We were curious how these tools would fare, given that mental health scenarios can be very subjective and nuanced.”

De Choudhury, Chandra, and their colleagues introduced their new framework at the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics on April 29, 2025.

Putting AI to the Test

Going into their research, De Choudhury and Chandra wanted to answer two main questions: First, can AI chatbots accurately detect whether someone is having side effects or adverse reactions to medication? Second, if they can accurately detect these scenarios, can AI agents then recommend good strategies or action plans to mitigate or reduce harm? 

The researchers collaborated with a team of psychiatrists and psychiatry students to establish clinically accurate answers from a human perspective and used those to analyze AI responses.

To build their dataset, they went to the internet’s public square, Reddit, where many have gone for years to ask questions about medication and side effects. 

They evaluated nine LLMs, including general purpose models (such as GPT-4o and LLama-3.1), and specialized medical models trained on medical data. Using the evaluation criteria provided by the psychiatrists, they computed how precise the LLMs were in detecting adverse reactions and correctly categorizing the types of adverse reactions caused by psychiatric medications.

Additionally, they prompted LLMs to generate answers to queries posted on Reddit and compared the alignment of LLM answers with those provided by the clinicians over four criteria: (1) emotion and tone expressed, (2) answer readability, (3) proposed harm-reduction strategies, and (4) actionability of the proposed strategies.

The research team found that LLMs stumble when comprehending the nuances of an adverse drug reaction and distinguishing different types of side effects. They also discovered that while LLMs sounded like human psychiatrists in their tones and emotions — such as being helpful and polite — they had difficulty providing true, actionable advice aligned with the experts. 

Better Bots, Better Outcomes

The team’s findings could help AI developers build safer, more effective chatbots. Chandra’s ultimate goals are to inform policymakers of the importance of accurate chatbots and help researchers and developers improve LLMs by making their advice more actionable and personalized. 

Chandra notes that improving AI for psychiatric and mental health concerns would be particularly life-changing for communities that lack access to mental healthcare.

“When you look at populations with little or no access to mental healthcare, these models are incredible tools for people to use in their daily lives,” Chandra said. “They are always available, they can explain complex things in your native language, and they become a great option to go to for your queries.

 “When the AI gives you incorrect information by mistake, it could have serious implications on real life,” Chandra added. “Studies like this are important, because they help reveal the shortcomings of LLMs and identify where we can improve.”

Citation: Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use, (Chandra et al., NAACL 2025).

Funding: National Science Foundation (NSF), American Foundation for Suicide Prevention (AFSP), Microsoft Accelerate Foundation Models Research grant program. The findings, interpretations, and conclusions of this paper are those of the authors and do not represent the official views of NSF, AFSP, or Microsoft.

Students from all majors are invited to register for the new Minor in Astrobiology at Georgia Tech. Welcoming its first enrolled students in Fall 2025, the minor is the latest degree offering from the College of Sciences and Georgia Tech Astrobiology Program.

Astrobio in focus

The Minor in Astrobiology will provide a broad, interdisciplinary introduction to the field of astrobiology while encouraging exploration beyond students’ primary fields of study. 

The program will foster the development of well-rounded scientists and engineers who will graduate with confidence in their knowledge and ability to engage in cutting-edge endeavors across the astrobiology discipline. 

The Minor in Astrobiology is open to all undergraduates at Georgia Tech. Students who enroll now and meet program requirements may graduate with the minor designation as early as the end of Fall 2025 term.

Enrolled students will learn about the physical and chemical conditions for development of a habitable planet; deeply understand Earth, space, and planetary science as well as the origin and evolution of life on Earth; and critically evaluate scientific issues related to astrobiology in media and popular culture.

Habitable Planet

The foundation of the new minor is the popular School of Earth and Atmospheric Sciences (EAS) course EAS 1601: Habitable Planet. Led by EAS Assistant Professor Frances Rivera-Hernández, the course enrolls up to 300 students a semester and expands options for study and career horizons. 

"Students from a lot of different majors enjoy 1601 where they get a true sense for the broadness of astrobio — stars, galaxies, biology, life beyond Earth, and how we create technology and science to explore those places,” Rivera-Hernández explains.

Like the new minor, EAS 1601 is also open to all majors. The class is offered in person across fall and spring semesters, and also available online during the summer term.

“We’re purposely very flexible with that course and the overall minor,” adds EAS Jean “Chris” Purvis Professor Jennifer Glass. “Students use EAS 1601 as a launchpoint to pick their interests for their wider field of study and to hone career interests — whether that’s space, biology, and autonomous rovers; hunting for chemical signs of life beyond our planet; or becoming an entrepreneur.” 

Over the past five years, students in the class frequently asked for formal degree offerings in astrobiology. Glass and fellow faculty partnered with then-graduate fellow Tyler Roche to explore the idea of a minor, leveraging Sutherland Dean's Chair funding in 2021 to officially launch the Georgia Tech Astrobiology Program in tandem with the Astrobiology Graduate Certificate Program, Astrobiology Fellows, and ExplOrigins young researcher group. 

Astrobio and beyond

The Astrobiology Program is now supported by the new Georgia Tech Space Research Initiative and co-directed by Rivera-Hernández and EAS Assistant Professor Christopher E. Carr, who holds a primary appointment in the Daniel Guggenheim School of Aerospace Engineering. 

Glass and Rivera-Hernández co-direct the Astrobiology Graduate Certificate Program, and co-direct the Astrobiology Minor with Nicholas Hud, Regents' Professor and Julius Brown Professor in the School of Chemistry and Biochemistry.

The team’s collaborative approach to interdisciplinary leadership mirrors unique opportunities ahead for students who are interested in exciting careers across the field of astrobiology and beyond. 

“The minor is designed across three broad divisions of study,” Glass adds. “Whether you’re curious about ‘Foundations of Life,’ want to dive deep into ‘Earth, Space, and Other Worlds,’ or are exploring career ideas for ‘Astrobiology in a Wider World,’ we’ve built a solid home for you in Astrobio at Tech.”

The Minor in Astrobiology will be listed in the 2025-26 Catalog beginning May 12.