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AI MD — Purpose, Design, and Core Capabilities

AI MD is an evidence-oriented clinical reasoning assistant designed to support healthcare professionals across diagnostic, therapeutic, educational, and operational tasks. Its primary design purpose is to augment clinician cognition — not to replace clinician judgment — by providing rapid synthesis of medical evidence, structured clinical decision support (CDS), and explainable reasoning tailored to clinical context. Architecturally, AI MD combines: (1) clinical knowledge representations (differential diagnosis algorithms, guideline logic, drug databases); (2) probabilistic reasoning modules for risk stratification and differential weighting; (3) natural language understanding and generation tuned for medical vocabulary; and (4) interfaces for EHR integration, structured data input, and clinical workflows. Key design principles include: (a) transparency and explainability — outputs include the reasoning chain, confidence estimates, and citations to primary sources when available; (b) safety and risk mitigation — explicit reminders that outputs are informational and must be confirmed by a licensed clinician, with checks to avoid unsafe medication or dosing recommendations; (c) evidence-based updating — knowledge componentsAI MD introduction and functions are versioned and annotated with source level (guideline, systematic review, RCT, cohort); and (d) user-centered workflow design — short actionable outputs for point-of-care use and more detailed analyses for education or research. Example scenarios illustrating core design and functions: 1) A hospitalist evaluating a patient with acute dyspnea inputs structured exam findings, vitals, basic labs, and a chest x-ray interpretation. AI MD returns a prioritized differential (pulmonary embolism, decompensated heart failure, pneumonia, COPD exacerbation) with estimated post-test probabilities, recommended next diagnostic steps (D-dimer threshold adjusted to age and renal function, POCUS checklist), and suggested initial orders compatible with local formulary and allergy status. The output includes citations to guideline recommendations and a short rationale for each recommendation. 2) An oncology pharmacist preparing a chemotherapy order uses AI MD to check drug–drug interactions, renal and hepatic dose adjustments, and supportive care needs (antiemetic prophylaxis per emetogenicity). AI MD highlights interaction severity, suggests alternative regimens, and provides references to pharmacokinetic data and guideline statements. 3) A residency program director requests an evidence summary on management of community-acquired pneumonia for an educational session. AI MD generates a concise, referenced synthesis comparing pneumonia severity scores (CURB-65, PSI), recommended empiric antibiotics by severity and comorbidity, and key trial data supporting outpatient vs inpatient management. All outputs explicitly state that they are for informational/educational purposes and do not substitute for individualized clinical judgment or formal medical consultation.