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A key step in medical diagnosis is giving the patient a universallyrecognized label (e.g. Appendicitis) which essentially assigns the patient to aclass(es) of patients with similar body failures. However, two patients havingthe same disease label(s) with high probability may still have differences intheir feature manifestation patterns implying differences in the requiredtreatments. Additionally, in many cases, the labels of the primary diagnosesleave some findings unexplained. Medical diagnosis is only partially aboutprobability calculations for label X or Y. Diagnosis is not complete until thepatient overall situation is clinically understood to the level that enablesthe best therapeutic decisions. Most machine learning models are data centricmodels, and evidence so far suggest they can reach expert level performance inthe disease labeling phase. Nonetheless, like any other mathematical technique,they have their limitations and applicability scope. Primarily, data centricalgorithms are knowledge blind and lack anatomy and physiology knowledge thatphysicians leverage to achieve complete diagnosis. This article advocates tocomplement them with intelligence to overcome their inherent limitations asknowledge blind algorithms. Machines can learn many things from data, but datais not the only source that machines can learn from. Historic patient data onlytells us what the possible manifestations of a certain body failure are.Anatomy and physiology knowledge tell us how the body works and fails. Both areneeded for complete diagnosis. The proposed Double Deep Learning approach,along with the initiative for Medical Wikipedia for Smart Machines, leads to AIdiagnostic support solutions for complete diagnosis beyond the limited dataonly labeling solutions we see today. AI for medicine will forever be limiteduntil their intelligence also integrates anatomy and physiology.

Disease Labeling via Machine Learning is NOT quite the same as Medical Diagnosis