What this term means here Deep learning is presented in this channel's material as the AI approach that ended up working — in contrast to the earlier strategy of observing experts and reducing their behavior into explicit rules, which is exactly how machine learning researchers started in AI and exactly what didn't work [^4caa6fda-b8ae-5bfd-ad34-331a298cb088@57]. Observing clinicians and then reducing how they operate to diagnostic rules — that's exactly how machine learning researchers started in AI and it's exactly what didn't work. What ended up working is deep learning. Why rules failed and deep learning worked [moment: 57] The parallel with clinical diagnosis The term does its work in an analogy: clinical diagnosis resists reduction to checklists the same way expert behavior resisted rule-based AI. Diagnoses like OCD are pragmatically real but have fuzzy boundaries — really a good clinician has a little bit of like a smell for it [^4caa6fda-b8ae-5bfd-ad34-331a298cb088@45]. Rule-based AI failed exactly where checklists fail; deep learning succeeded because it learns patterns rather than following written rules. That shift — from rules to intuition — is the bridge the material draws from psychiatry to the future of AI in medicine. - Rule-based AI (reducing expert behavior to diagnostic rules) failed. - Deep learning is the approach that ended up working, by learning patterns instead of following written rules [^4caa6fda-b8ae-5bfd-ad34-331a298cb088@57]. - The term anchors the analogy: rule-based AI failed exactly the way checklists fail in clinical diagnosis. [^4caa6fda-b8ae-5bfd-ad34-331a298cb088@57]: The Future of AI in Medicine: From Rules to Intuition | Awais Aftab, Psychiatrist and writer, at 0:57. [^4caa6fda-b8ae-5bfd-ad34-331a298cb088@45]: The Future of AI in Medicine: From Rules to Intuition | Awais Aftab, Psychiatrist and writer, at 0:45.