AI Innovations Poised to Revolutionize Lyme Disease Diagnosis and Treatment

CHICAGO — In a significant development presented at ADLM 2025, the Association for Diagnostics & Laboratory Medicine's annual scientific meeting, researchers revealed a groundbreaking blood test utilizing artificial intelligence (AI) to diagnose Lyme disease with greater accuracy and speed than current methods. The new test, which aims to improve patient outcomes, demonstrates a sensitivity and specificity exceeding 90%, marking a substantial advancement over the traditional two-tier serology testing, which only identifies early Lyme disease accurately about 30% of the time.

Each year, approximately 475,000 cases of Lyme disease are diagnosed in the United States, a number expected to rise due to climate change extending the habitats of ticks, the disease's primary carriers. Early detection is critical, as untreated Lyme disease can lead to long-term health issues such as chronic fatigue, neurocognitive disorders, and arthritis. According to Holly Ahern, a microbiologist and the chief scientific officer at ACES Diagnostics, "With the new test, nine out of ten patients will receive a correct diagnosis, significantly reducing the risk of chronic illness."

The innovative test is based on a study involving rhesus macaque monkeys, which have a comparable immune response to humans regarding Lyme disease. The research team, led by Ahern, developed a panel that detects ten specific proteins (antigens) involved in the Lyme disease infection. This new method aims to replace the cumbersome two-tier approach, which can require up to four separate tests.

In a clinical trial, the researchers analyzed blood samples from 123 Lyme disease patients and 197 uninfected individuals. They enhanced the test's performance through machine learning, which identifies unique immune patterns. "You and I might be infected by the same bacteria, but our antibody responses can differ," Ahern explained. The result was an algorithm that flagged over 90% of early infections, a dramatic increase from the mere 27% accuracy offered by standard methods.

The new test, which is expected to be commercially available by late 2026, is designed to be cost-effective and compatible with standard laboratory equipment, promising accessibility for healthcare providers across various settings.

In addition to the Lyme disease diagnostic advancements, another study presented at ADLM 2025 explored the use of generative AI tools to assist adolescents in accessing reliable medical information. This research focused on Medicine-GPT, a customized version of ChatGPT designed to engage young users seeking health-related answers they may hesitate to discuss with adults. Austin Jin, a high school research intern at Weill Cornell Medicine, stated, "Medicine-GPT shows promise as a powerful tool for addressing adolescent health inquiries," outperforming the standard ChatGPT-4 on measures of completeness, reasoning, and overall helpfulness.

Jin's team gathered over 100 clinical questions from Reddit's 'Ask Doctors' forum, focusing on users aged 10-19. The custom model achieved a completeness score of 66.6% and reasoning score of 60%, compared to significant lower scores from ChatGPT-4. However, Jin cautioned that while these tools can provide accurate information, they may overwhelm users with potential diagnoses, underscoring the need for responsible usage and proper clinician guidance.

The ADLM 2025 event, taking place from July 27-31 at the McCormick Place Convention Center in Chicago, also features discussions on the integration of AI in clinical settings and the impact of misinformation in medical contexts. The expo showcases over 800 exhibitors, highlighting the latest advancements in diagnostic technology, including AI and point-of-care solutions.

As the integration of AI in medical diagnostics continues to expand, these advancements signal a shift towards more personalized and effective healthcare solutions. The potential for improved early detection and treatment of Lyme disease exemplifies the promise of technology in addressing pressing public health challenges.