{
  "abstract": "In this issue of the Journal of Clinical Pathology, Dylan Windell and colleagues present a machine-learning model for evaluating liver inflammation in patients with metabolic-dysfunction-associated steatohepatitis (MASH) and autoimmune hepatitis (AIH).1 Unlike many recent pathology artificial intelligence (AI) systems that lean on weakly or self-supervised paradigms popular in tumour biology, this is a fully (indeed, deeply) supervised model designed to think like a pathologist.2",
  "authors": [
    {
      "affiliations": [
        "Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA"
      ],
      "name": "Vikram Deshpande"
    }
  ],
  "title": "Thinking like a pathologist: anatomy-anchored artificial intelligence for liver pathology",
  "uid": "73d27374-cc14-5755-a04e-02a8916a01bb"
}