SOTA genome interpretation with agentic AI: An interstitial lung disease case study | Gamow Labs
July 6, 2026 · Daniel McKinnon
SOTA genome interpretation with agentic AI: An interstitial lung disease case study
Over the past few years, overwhelming evidence has emerged that expanding access to whole genome sequencing improves clinical and economic outcomes in the NICU, a setting highly enriched for genetic disease. However, sequencing is still largely limited to top academic centers due to the:
complexity of ordering and interpreting results
expense and difficulty filing for reimbursement despite near universal payer coverage
time required to receive results or reanalysis
We at Gamow Labs hypothesize that correctly harnessed AI agents can perform the task of diagnosing a genetic disease at or above human performance at a fraction of the cost and in a fraction of the time at great scale. In this blog post, we present early evidence that this hypothesis is true.
Through a blinded collaboration with Pawel Stankiewicz at Baylor College of Medicine, we accessed the raw reads (FASTQ files) of 46 individuals, who were either infants lost to interstitial lung disease or their healthy family members. In all cases, the initial molecular diagnosis was missed by the first-line clinical genomics lab and escalated to Dr. Stankiewicz, the leading expert on the genetic causes of these diseases, for reanalysis.
We provided the Gamow Labs system, George-0.1, with the raw FASTQ files alongside a simple phenotype provided by Dr. Stankiewicz: “the patient suffered from a lethal lung disorder like alveolar capillary dysplasia, acinar dysplasia, or congenital alveolar dysplasia” (this phenotype was obviously wrong for the healthy family members, but we had no idea that these were included). The agent generated a molecular diagnosis classified using the ACMG guidelines.
We then shared the results with Dr. Stankiewicz for scoring. A true positive would be marked as correct if the variant matched his answer key with pathogenic or likely pathogenic scoring. A true negative would be marked correct if there were either no diagnosis or variant of uncertain significance (VUS) (the system is tuned for recall, so it is not surprising to find some VUSes called for healthy people when presented with a diseased phenotype).
The results were encouraging. Every case, with the exception of the healthy family members, was incorrectly diagnosed by the first-line clinical labs (this is why they were escalated to Dr. Stankiewicz). Dr. Stankiewicz’s lab subsequently solved 19/26, leaving 7 as mysteries. Gamow Labs’ George-0.1 matched the performance of Dr. Stankiewicz’s lab, reproducing the molecular diagnosis of all 19 cases while contributing 2 additional solutions. These initial results, summarized in Table 1, suggest that agentic AI like George-0.1 can exceed the performance of clinical whole genome sequencing labs and match or even exceed specialized human experts.
Furthermore, we independently validated the recent NEJM AI paper authored by Boston Children’s and OpenAI on this dataset. They showed that consumer chatbots–ChatGPT 5.5 Pro in our study and o3 Deep Research in theirs–have significant utility in rare disease diagnosis. We observed the same. While performance lagged Gamow Labs’ George-0.1, uploading a phenotype and genome into ChatGPT 5.5 Pro outperforms clinical labs on these challenging cases.
We attempted to extend the experiment to Claude.ai (Opus 4.8) and Gemini.google.com (Gemini 3.5 Flash), two other popular consumer tools, but neither system succeeded in the first three attempts.
Total<br>Standard of care (clinical lab) correct<br>Gamow Labs correct<br>ChatGPT 5.5 Pro correct*
Healthy family members<br>20<br>N/A<br>20 (100%)<br>6 (30%)
Published known diagnosis<br>11<br>0 (0%)<br>11 (100%)<br>9 (82%)
Unpublished known diagnosis<br>0 (0%)<br>8 (100%)<br>5 (63%)
Previously unknown diagnosis<br>0 (0%)<br>2 (100%)<br>2 (100%)
Still unknown diagnosis<br>0 (0%)<br>N/A
Table 1: summary of performance of three approaches to genome interpretation. These are all hard cases involving complex structural or intronic variants that were initially missed by various clinical labs. ChatGPT 5.5 Pro significantly improved diagnostic yield over clinical workflow and Gamow Labs’ George-0.1 improved further.
* pre-called vcf files passed to chatbot vs. raw reads to Gamow Labs’ George-0.1, making the task easier
** 4/5 unknown cases received molecular diagnoses that were obviously wrong due to the reasons discussed in Case Study #2
Case study #1: Clinical lab false negative
ACD209.3, published in 2022 (only published cases discussed here), was originally sequenced by Rady Children’s Institute for Genomic Medicine. Alignment and variant calling were performed using a standard Illumina DRAGEN pipeline in addition to a combination of open-source CNV/SV callers. Because this ensemble of callers generates a significant number...