To elaborate, errors go beyond data and reach into model design. Two simple examples:
1. Nucleotides are a form of tokenization and encode bias. They're not as raw as people assume. For example, classic FASTA treats modified and canonical C as identical. Differences may alter gene expression -- akin to "polish" vs. "Polish".
2. Sickle-cell anemia and other diseases are linked to nucleotide differences. These single nucleotide polymorphisms (SNPs) mean hard attention for DNA matters and single-base resolution is non-negotiable for certain healthcare applications. Latent models have thrived in text-to-image and language, but researchers cannot blindly carry these assumptions into healthcare.
There are so many open questions in biomedical AI. In our experience, confronting them has prompted (pun intended) better inductive biases when designing other types of models.
We need way more people thinking about biomedical AI.
Comments
To elaborate, errors go beyond data and reach into model design. Two simple examples:
1. Nucleotides are a form of tokenization and encode bias. They're not as raw as people assume. For example, classic FASTA treats modified and canonical C as identical. Differences may alter gene expression -- akin to "polish" vs. "Polish".
2. Sickle-cell anemia and other diseases are linked to nucleotide differences. These single nucleotide polymorphisms (SNPs) mean hard attention for DNA matters and single-base resolution is non-negotiable for certain healthcare applications. Latent models have thrived in text-to-image and language, but researchers cannot blindly carry these assumptions into healthcare.
There are so many open questions in biomedical AI. In our experience, confronting them has prompted (pun intended) better inductive biases when designing other types of models.
We need way more people thinking about biomedical AI.