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pres.title = "Orris AI in Medical Education";
pres.author = "MD Student General Club";

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// ── HELPERS ────────────────────────────────────────────────────────────────
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// ══════════════════════════════════════════════════════════════════════════
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// ══════════════════════════════════════════════════════════════════════════
// SLIDE 2 — AGENDA
// ══════════════════════════════════════════════════════════════════════════
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  slideTitle(s, "What We'll Cover Today");
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  const items = [
    ["01", "The AI Landscape in Medicine", "Where we are in 2026"],
    ["02", "What Is Orris?", "Architecture, design, and medical-first routing"],
    ["03", "Core Capabilities", "Knowledge retrieval, PubMed, clinical reasoning"],
    ["04", "Evidence & Benchmarks", "Systematic review findings on LLMs in clinical medicine"],
    ["05", "Limitations & Risks", "Hallucination, bias, over-reliance, deskilling"],
    ["06", "Critical Appraisal Framework", "How to evaluate any AI tool"],
    ["07", "The Road Ahead", "Responsible integration for future physicians"],
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// ══════════════════════════════════════════════════════════════════════════
// SLIDE 3 — AI LANDSCAPE 2026
// ══════════════════════════════════════════════════════════════════════════
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  sectionBar(s, "Context");
  slideTitle(s, "The AI Landscape in Medicine — 2026", "From research curiosity to clinical infrastructure");
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    { x: 0.4,  y: 1.75, icon: "📊", title: "Radiology AI", body: "FDA-cleared algorithms now exceed 1,200. Chest X-ray triage, nodule detection, fracture flagging — widely deployed." },
    { x: 3.5,  y: 1.75, icon: "💊", title: "Drug Discovery", body: "AlphaFold3 + generative chemistry. Two AI-designed molecules have entered Phase II trials as of 2026." },
    { x: 6.6,  y: 1.75, icon: "🤖", title: "LLMs at Bedside", body: "GPT-4 / Gemini / Med-PaLM integrated into EHR systems. Note writing, discharge summaries, prior-auth automation." },
    { x: 0.4,  y: 3.55, icon: "📚", title: "Education Tools", body: "AI tutors, virtual patients, adaptive Q-banks. 67% of US medical schools report formal AI curriculum (2025 survey)." },
    { x: 3.5,  y: 3.55, icon: "🔬", title: "Research Synthesis", body: "AI-assisted systematic reviews cut completion time from months to days. PubMed has >35M indexed citations." },
    { x: 6.6,  y: 3.55, icon: "⚠️", title: "Critical Gaps", body: "Real-world performance often drops vs. benchmarks. Equity, hallucination, and liability remain unresolved." },
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  footer(s, "Stanford-Harvard State of Clinical AI Report, 2026  |  Shool et al., BMC Med Inform Decis Mak, 2025 (PMID 40055694)");
}

// ══════════════════════════════════════════════════════════════════════════
// SLIDE 4 — WHAT IS ORRIS?
// ══════════════════════════════════════════════════════════════════════════
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  sectionBar(s, "Introduction");

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    { title: "Medical-First Routing", body: "Any query containing clinical, anatomical, pharmacological, or biomedical terms is routed FIRST through a curated textbook library — before any general LLM answer is generated." },
    { title: "Live Evidence Layer", body: "Integrated PubMed search with auto-ranking by evidence tier: Systematic Reviews > Meta-Analyses > RCTs. Returns PMIDs, abstracts, and flags retracted papers." },
    { title: "Textbook Knowledge Base", body: "Indexed medical library with FTS5 full-text search across headings and paragraphs. Answers cite specific books and page numbers — traceable and auditable." },
    { title: "Transparent Uncertainty", body: "Orris is designed to say 'I don't know' or flag gaps. It cannot hallucinate citations — all references come from verified database queries with real PMIDs." },
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}

// ══════════════════════════════════════════════════════════════════════════
// SLIDE 5 — CORE CAPABILITIES (workflow diagram)
// ══════════════════════════════════════════════════════════════════════════
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  sectionBar(s, "Capabilities");
  slideTitle(s, "How Orris Works — The Query Pipeline");
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    { label: "PubMed\nSearch", color: "0F766E", icon: "🧪" },
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  // Feature cards below
  const features = [
    { title: "Quick Answer First", body: "Always provides a preliminary answer in seconds from training knowledge, then enriches with live sources." },
    { title: "Budget-Aware", body: "Tracks tool call budgets per session. Prioritises parallel queries to maximise information density per turn." },
    { title: "Cite-as-You-Go", body: "Every textbook reference is tied to a specific book, block, and line number. PubMed results include PMID." },
    { title: "Image Retrieval", body: "Fetches clinical images — microscopy, anatomy, pathology diagrams — directly from textbook databases." },
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// ══════════════════════════════════════════════════════════════════════════
// SLIDE 6 — USE CASES FOR MD STUDENTS
// ══════════════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  lightSlide(s);
  sectionBar(s, "Practical Applications");
  slideTitle(s, "Orris for Medical Students — Use Cases");
  accentLine(s, 1.65);

  const cases = [
    { icon: "📋", title: "Clinical Reasoning Support", body: "Ask about differential diagnosis frameworks, pathophysiology mechanisms, or management stepladders — Orris retrieves from textbooks and supplements with recent RCTs." },
    { icon: "💊", title: "Pharmacology Quick Reference", body: "Drug mechanisms, interactions, dosing principles, and side-effect profiles sourced from indexed pharmacology texts with PubMed safety alerts." },
    { icon: "🔬", title: "Pre-Rounds Literature Review", body: "Query the latest systematic reviews on your patient's condition in <60 seconds. Orris ranks by evidence tier and flags retracted papers automatically." },
    { icon: "📝", title: "Exam Preparation", body: "Topic summaries, concept clarifications, and MCQ explanations grounded in textbook content — not hallucinated web snippets." },
    { icon: "🧠", title: "Case-Based Learning", body: "Work through clinical vignettes with Orris as a discussion partner. It surfaces gaps in reasoning without generating false certainty." },
    { icon: "📊", title: "Critical Appraisal Aid", body: "Paste a PMID or describe a trial — Orris helps evaluate study design, bias risks, and clinical applicability aligned with EBM frameworks." },
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// ══════════════════════════════════════════════════════════════════════════
// SLIDE 7 — EVIDENCE (LLMs in clinical medicine)
// ══════════════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  lightSlide(s);
  sectionBar(s, "Evidence Base");
  slideTitle(s, "What the Evidence Says — LLMs in Clinical Medicine", "Shool et al., Systematic Review, BMC Med Inform Decis Mak, 2025 (PMID 40055694)");
  accentLine(s, 1.65);

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  const findings = [
    "LLMs perform well on structured MCQ exams (USMLE-style) — often passing Step 1 thresholds",
    "Performance degrades significantly on open-ended clinical reasoning tasks",
    "Models commit to answers confidently even under high uncertainty",
    "Outperformed medical students on static tests; closer to junior doctors on complex reasoning",
    "Real-world performance consistently lower than benchmark scores",
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    { val: "40+", label: "systematic reviews / meta-analyses on LLMs in clinical medicine (2023-2026)" },
    { val: "↓", label: "Performance drops when tasks require managing incomplete or evolving information" },
    { val: "67%", label: "of US medical schools have formal AI curriculum — but few teach critical appraisal" },
    { val: "43%", label: "of medical students report 'unclear evidence of AI impact on performance' as a barrier" },
  ];

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  footer(s, "Sources: Shool et al. 2025 (PMID 40055694) | Stanford-Harvard State of Clinical AI Report 2026 | PMC12265092");
}

// ══════════════════════════════════════════════════════════════════════════
// SLIDE 8 — LIMITATIONS & RISKS
// ══════════════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  darkSlide(s);
  sectionBar(s, "Critical Appraisal — Limitations");

  s.addText("Limitations & Risks of AI in Clinical Settings", {
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  s.addText("What the evidence tells us — and what Orris still cannot do", {
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  const risks = [
    { label: "Hallucination", severity: "HIGH", color: C.red, body: "LLMs generate plausible-sounding but factually wrong content. Orris mitigates this via database-grounded retrieval, but no system is hallucination-free." },
    { label: "Overconfidence", severity: "HIGH", color: C.red, body: "AI systems commit strongly to answers even when ambiguity is high. NEJM AI (McCoy et al., 2025) showed models perform closer to students than experienced clinicians under uncertainty." },
    { label: "Deskilling Risk", severity: "MED", color: C.amber, body: "Bypassing cognitive steps (differential generation, ambiguous finding interpretation) may impair System 2 reasoning development. (Dove Med Press, 2025)" },
    { label: "Training Data Cutoff", severity: "MED", color: C.amber, body: "Knowledge may lag by months to years. Orris addresses this with live PubMed search, but tool call budgets may limit depth." },
    { label: "Equity & Bias", severity: "MED", color: C.amber, body: "Training data over-represents certain populations. Diagnostic accuracy may vary by patient demographics, rare presentations, or resource-limited contexts." },
    { label: "Liability Gap", severity: "LOW", color: C.green, body: "No clear regulatory framework for AI-assisted clinical decisions. The clinician remains legally and ethically responsible — AI is a tool, not a consultant." },
  ];

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}

// ══════════════════════════════════════════════════════════════════════════
// SLIDE 9 — CRITICAL APPRAISAL FRAMEWORK
// ══════════════════════════════════════════════════════════════════════════
{
  const s = pres.addSlide();
  lightSlide(s);
  sectionBar(s, "Framework");
  slideTitle(s, "How to Critically Appraise Any AI Medical Tool", "A 5-question checklist for MD students");
  accentLine(s, 1.65);

  const questions = [
    {
      num: "Q1", q: "Where does the knowledge come from?",
      good: "Indexed textbooks, PubMed, traceable citations",
      bad: "Opaque training data, no source disclosure"
    },
    {
      num: "Q2", q: "How is uncertainty handled?",
      good: "Tool says 'I don't know', flags low confidence",
      bad: "Always produces a fluent, confident answer"
    },
    {
      num: "Q3", q: "Has it been validated on real patients?",
      good: "Prospective studies, real-world deployment data",
      bad: "Benchmark-only, MCQ-only evaluation"
    },
    {
      num: "Q4", q: "Is there a human in the loop?",
      good: "AI augments clinical judgment, not replaces it",
      bad: "Autonomous decision-making without oversight"
    },
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      num: "Q5", q: "What population was it trained/tested on?",
      good: "Diverse cohorts, subgroup analysis reported",
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// ══════════════════════════════════════════════════════════════════════════
// SLIDE 10 — THE ROAD AHEAD
// ══════════════════════════════════════════════════════════════════════════
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    { year: "Clerkships", title: "Supervised AI Integration", body: "Use AI tools under attending supervision. Document when AI was consulted and how it influenced (or didn't influence) decisions." },
    { year: "Residency", title: "Feedback Loops", body: "AI tools should learn from clinical outcomes. Residents are positioned to contribute high-quality training signal through structured feedback." },
    { year: "Practice", title: "Regulatory Maturity", body: "Expect formal AI credentialing, liability frameworks, and mandatory transparency requirements. The regulatory wave is coming." },
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// ══════════════════════════════════════════════════════════════════════════
// SLIDE 11 — CLOSING / KEY TAKEAWAYS
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    { num: "2", text: "LLMs pass USMLE benchmarks but underperform in real-world uncertainty — don't confuse exam performance with clinical competence." },
    { num: "3", text: "Medical-first routing and database-grounded retrieval reduce (but don't eliminate) hallucination risk. Always verify critical facts." },
    { num: "4", text: "AI literacy is a professional competency. You will be expected to evaluate, justify, and document your use of AI tools in clinical practice." },
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// ══════════════════════════════════════════════════════════════════════════
// SLIDE 12 — REFERENCES
// ══════════════════════════════════════════════════════════════════════════
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    "1.  Shool S, Adimi S, Saboori Amleshi R. A systematic review of large language model (LLM) evaluations in clinical medicine. BMC Med Inform Decis Mak. 2025 Mar 7. PMID: 40055694",
    "2.  Stanford-Harvard State of Clinical AI Report. Published January 2026. medicine.stanford.edu",
    "3.  AL-Qahtanee H. Artificial Intelligence in Medical Education: A Cautionary Advice for Medical Students. World J Surg Surgical Res. 2025;8:1599.",
    "4.  AI in medical student education: impact on clinical skill acquisition and reasoning. Adv Med Educ Pract. 2025;16:1169-1179.",
    "5.  Integrating artificial intelligence into medical education: a roadmap informed by a survey of faculty and students. PMC12265092. 2025.",
    "6.  McCoy TH et al. AI reasoning under uncertainty compared to medical students and physicians. NEJM AI. 2025.",
    "7.  Johri A et al. Clinical AI performance with incomplete information and follow-up questions. Nature Medicine. 2025.",
    "8.  Mikkonen K et al. AI Technologies Supporting Nurses' Clinical Decision-Making: Systematic Review. J Clin Nurs. 2026 Apr. PMID: 41292265",
  ];

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