While the cause is noble, the medical detection of child abuse faces serious issues with undetected and unacknowledged false positives [2], since ground truth is almost never knowable. The prevailing idea is that certain medical findings are considered proof beyond reasonable doubt of violent abuse, even without witnesses or confessions (denials are extremely common). These beliefs rest on decades of medical literature regarded by many as low quality because of methodological flaws, especially circular reasoning (patients are classified as abuse victims because they show certain medical findings, and then the same findings are found in nearly all those patients—which hardly proves anything [3]).
I raise this point because, while not exactly software bugs, we are now seeing black-box AIs claiming to detect child abuse with supposedly very high accuracy, trained on decades of this flawed data [4, 5]. Flawed data can only produce flawed predictions (garbage in, garbage out). I am deeply concerned that misplaced confidence in medical software will reinforce wrongful determinations of child abuse, including both false positives (unjust allegations potentially leading to termination of parental rights, foster care placements, imprisonment of parents and caretakers) and false negatives (children who remain unprotected from ongoing abuse).
[1] https://hs.memberclicks.net/executive-committee
[2] https://news.ycombinator.com/item?id=37650402
[3] https://pubmed.ncbi.nlm.nih.gov/30146789/
[5] https://www.sciencedirect.com/science/article/pii/S002234682...