Artificial intelligence is being utilized across a variety of industries to reduce human workload, speed up workflows and improve output. Within the field of molecular pathology, AI is being used in part to improve diagnostic processes and accuracy. AI has the potential to not only automate tasks, but also to enhance clinical decision making.
Innovative studies in diagnostic applications of artificial intelligence will be presented at the Association for Molecular Pathology (AMP) 2025 Annual Meeting & Expo, taking place Nov. 11–15 in Boston.
Journalists are invited to attend the meeting in person or sign up for online access to all press materials. Details are available at https://amp25.amp.org/media/media-information/.
Below are some of the potential AI applications that will be shared by leading experts in molecular diagnostics at AMP 2025.
93% diagnostic accuracy achieved by AI classifier for cancer
RNA sequencing has the potential to be used as a comprehensive diagnostic test to help rapidly provide more accurate cancer diagnoses. However, errors arising from different tissue storage and preparation methods reduce accuracy.
Researchers from The Hospital for Sick Children developed a web platform incorporating an AI classifier designed to handle heterogeneous datasets and integrate RNA sequencing into clinical workflows.
The model achieved 93% diagnostic accuracy on subtypes covered by the platform. The system can also adapt and incorporate new subtypes, increasing its accuracy with each new sample. The goal for the platform is to cover new subtypes with only five reference samples.
The researchers aim to scale up the platform, covering a more diverse set of benign and malignant entities, allowing them to bridge the gap between research and real-world clinical diagnostics.
This work was led by first author Pedro Lemos Ballester, Ph.D., at The Hospital for Sick Children, who will give a presentation about it during a poster session at 9:15 a.m. on Saturday, Nov. 15, at the Thomas M. Menino Convention and Exhibition Center in Boston.
Earlier, noninvasive diagnosis using spinal fluid enabled by AI
Central nervous system tumor tissue biopsies are invasive and difficult to repeat, limiting their role in diagnosis. Cerebrospinal fluid–derived circulating tumor DNA offers a noninvasive alternative.
Researchers at Soonchunhyang University in South Korea created two AI models to classify samples: a dense neural network trained on mutation data from 12 key genes via next-generation sequencing and a convolutional neural network trained on standardized MRI images. Both models showed strong accuracy, the researchers said. Combining outputs from both models improved prediction and classification accuracy.
This inverted pipeline enables accurate mutation prediction and informed treatment planning before surgery, allowing surgeons to anticipate tumor biology rather than wait for postoperative tissue analysis. By empowering earlier decision-making and facilitating the selection of targeted therapeutic options at a preoperative stage, this approach shifts neuro-oncology toward a more proactive, precision-based model of care
This work will be presented by corresponding author Jieun Kim, M.D., Ph.D., from Soonchunhyang University, during a poster session at 9:15 a.m. on Saturday, Nov. 15, at the Thomas M. Menino Convention and Exhibition Center in Boston.
AI reveals chromosomal changes in blood cancer patients
GATA2 deficiency syndrome is a rare autosomal dominant genetic disorder causing predisposition to immunodeficiency and myeloid malignancy. It causes variable risk for blood cancers such as myelodysplastic syndromes and acute myeloid leukemia, with complex genetic changes driving disease progression.
Researchers at Wake Forest University School of Medicine used an AI-trained karyotyping algorithm in clinical cytogenetics to analyze chromosomal abnormalities in GATA2 deficiency syndrome-related leukemia. AI enabled rapid generation and review of hundreds of images, improving detection and confidence in identifying complex clonal chromosome rearrangements.
The AI-assisted karyotyping revealed detailed clonal evolution over time in a patient’s acute myeloid leukemia, capturing multiple chromosomal changes that tracked disease progression. Overall, the algorithm provided insights into personalized disease progression and better understanding of GATA2 deficiency syndrome.
This work was led by first author Lynne Rosenblum, Ph.D., at Wake Forest University School of Medicine, who will give a presentation about it during a poster session at 9:15 a.m. on Saturday, Nov. 15, at the Thomas M. Menino Convention and Exhibition Center in Boston.
With AI, doctors move toward more personalized oncology care
In diagnosing cancer, doctors often look at hematoxylin and eosin (H&E)-stained slides to see cell shape and structure alongside genetic or molecular tests (such as sequencing DNA or RNA) to see mutations or gene activity. While crucial to diagnosis, genetic testing is expensive, slow and requires additional tissue samples from patients.
At Augusta University, researchers created a computational framework to train and compare AI models to look at slide images from patients and predict genomic and transcriptomic information directly from the images. The framework successfully compared different AI models and could handle prediction tasks such as identifying gene activity and prognosis. When patient clinical information was incorporated, the predictions became more informative. The researchers found that different AI models perform better or worse depending on the specific goal or data type, requiring future standardization of testing and benchmarks.
Long-term, researchers hope to make obtaining molecular-level information about tumors from slide images possible. This framework marks a step closer to precision medicine guided by patient information being available on a larger scale.
This work was overseen by Ravindra Kolhe, M.D., Ph.D., at Augusta University. First author Pankaj Ahluwalia, Ph.D., will give a presentation about it during a poster session at 9:15 a.m. on Friday, Nov. 14, at the Thomas M. Menino Convention and Exhibition Center in Boston.
About AMP
The Association for Molecular Pathology was founded in 1995 to provide structure and leadership to the emerging field of molecular diagnostics. AMP’s more than 3,100 members practice various disciplines of molecular diagnostics, including bioinformatics, infectious diseases, inherited conditions and oncology. Our members are pathologists, clinical laboratory directors, basic and translational scientists, technologists and trainees who practice in a variety of settings, including academic and community medical centers, government and industry. For more information, visit www.amp.org.