Is there published research on GMDB?

Yes, GestaltMatcher Database (GMDB) and GestaltMatcher AI have been featured in several peer-reviewed publications on different research topics and clinical implementation.

You can find a list of all publications referencing GestaltMatcher on our Publications page. Here is a list of selected key publications:

Methodology of GestaltMatcher AI

• Hsieh TC, Bar-Haim A, Moosa S, et al. Nature Genetics (2022)

  Introduced the next-generation phenotyping approach to facial analysis, establishing the core methodology of GestaltMatcher AI.

Clinical utility

• Schmidt A, Danyel M, Grundmann K, et al. Nature Genetics (2024)

  Demonstrated how integrating GestaltMatcher into clinical workflows improves diagnostic yield and novel gene discoveries, even in ultra-rare diseases.

Lumping and splitting analysis

• Mak CCY, Klinkhammer H, Choufani S, et al. eBioMedicine (2025)

  Presented a workflow using GMDB data to split and lump phenotypic clusters within the same gene, illustrating how facial phenotype analyses can distinguish subgroups and refine genotype–phenotype correlations.

Laboratory utility

• Hsieh TC, Mensah MA, Pantel JT, et al. Genetics in Medicine (2019)

  Showed that combining facial analysis with exome sequencing improves variant ranking, introducing AI-supported diagnostic pipelines in rare disease genomics.

• Bhasin MA, et al. Genes (2024)

  Gives an example of how GestaltMatcher can be implemented in variant analysis platforms.

• Lesmann H, Klinkhammer H, Krawitz, PM. Medizinische Genetik (2024)

  Discusses how the ACMG PP4 criterion may change the interpretation of variants of uncertain significance (VUS).

Application in diverse populations

• Arlt A, et al. American Journal of Medical Genetics Part A (2024)

  Discusses how GestaltMatcher AI was used to identify (novel) pathogenic variants in Nigerian children with Cornelia de Lange syndrome.