{
  "id": 17352538,
  "title": "Can AI tell when patients don't need medical tests? Israeli study puts chatbots to the test",
  "url": "https://urgent.news/2026/10/31/can-ai-tell-when-patients-dont-need-medical-tests-israeli-study-puts",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-31T06:34:51.000Z",
  "source": {
    "name": "Jerusalem Post",
    "slug": "jerusalem-post",
    "url": "https://www.jpost.com/health-and-wellness/article-913210"
  },
  "original_language": "en",
  "account": "Israeli researchers have evaluated six leading language models, including those developed by Google, Anthropic and OpenAI, in determining when medical tests are unnecessary. The study, published in Annals of Internal Medicine, found that these AI systems successfully identified situations where testing or treatment is redundant, though they occasionally recommended such measures in cases where they were not warranted.\n\nThe researchers examined 15 Choosing Wisely recommendations, creating 15 scenarios for each. For every recommendation, they developed five scenarios where action was necessary and ten where restraint was appropriate. They then assessed how the models handled these cases.\n\nIn most instances, the AI systems correctly identified when a test or treatment was not required. However, they occasionally recommended unnecessary procedures, particularly in cases involving palpitations and unimportant nodules discovered during routine examinations. For example, the AI systems recommended electrocardiograms (EKGs) in 28.3% of cases where they were not medically necessary, and ultrasounds for incidentally found nodules in 42.8% of instances.\n\nThe study highlights the challenge of balancing overtesting and underdiagnosis, as AI models often reflect the hesitations and uncertainties found in human clinicians. The researchers suggest that further research is needed to determine whether AI assistants can maintain appropriate clinical restraint when interacting with anxious or insistent patients.",
  "summary": "“One of the hardest parts of practicing medicine is knowing when to hold back,” explained Ariel. Patients find comfort in an extra test, but it can set off unnecessary worry and treatment.",
  "key_points": [],
  "editors_take": "Integrating large language models into clinical decision-making offers a way to reduce redundant procedures, though their tendency toward overtesting mirrors the cautious hesitations often seen in human clinicians.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}