If you're a medical student, you've had this moment: it's 10 PM, you just sat through a two-hour cardiology lecture, and now you're staring at 85 PowerPoint slides that somehow need to become Anki cards before your next exam block.
You know spaced repetition works. You know Anki is the gold standard. But the bottleneck isn't reviewing cards — it's making them.
Let's walk through both approaches: the manual grind most students default to, and a faster way that's recently become possible.
The Manual Way (And Why It Takes So Long)
Here's the typical workflow most med students use to turn lectures into Anki cards:
- Open the lecture slides side-by-side with Anki
- Read through each slide, identifying testable facts
- Decide on a card type — basic, cloze, or image occlusion
- Type each card by hand, formatting it properly
- Add tags for the lecture, system, and topic
- Handle diagrams — screenshot, crop, paste, add occlusion boxes
- Review for quality — fix typos, check accuracy, remove duplicates
For a typical 60-slide lecture, this process takes 2 to 3 hours. That's not an exaggeration — it's the number students consistently report on r/medicalschoolanki and various med school forums.
The math gets bleak fast. If you have 5 lectures a week, that's 10-15 hours just making cards. Add in the actual review time and you're spending more time on Anki logistics than on learning.
Common Shortcuts (And Their Tradeoffs)
Students have tried various workarounds:
- Pre-made decks (AnKing, Zanki): Excellent for board prep, but they don't cover your specific lectures. You still need cards for your professor's emphasis, class-specific content, and your own slides.
- Copy-pasting bullet points: Fast, but produces low-quality cards. A bullet point isn't the same as a well-crafted cloze deletion.
- Splitting the work across study groups: Works sometimes, but inconsistent quality and formatting. Plus, making your own cards is part of the learning process.
- Skipping Anki entirely: Some students switch to just re-reading slides. Studies consistently show this is the least effective study method.
None of these fully solve the problem. You either sacrifice quality, coverage, or learning.
What Makes a Good Lecture-Based Anki Card?
Before we talk about faster methods, it helps to understand what we're aiming for. Good lecture cards have a few properties:
Use the right card type for the concept:
- Cloze deletions for facts, values, definitions, and relationships ("The SA node fires at 60-100 bpm")
- Q&A cards for reasoning and mechanisms ("Why does hyperkalemia cause peaked T waves?")
- Image occlusion for diagrams, anatomy, and labeled figures
Test one concept per card. The "minimum information principle" — each card should test a single, atomic piece of knowledge.
Include context. Don't just write "60-100 bpm." Write "The sinoatrial (SA) node generates impulses at 60-100 bpm." Context makes retrieval practice more effective.
Cover diagrams, not just text. A huge amount of testable information in medical lectures lives in figures — anatomical diagrams, pathology slides, flowcharts. If you're only making text cards, you're missing a significant portion of the material.
The Fast Way: AI-Powered Card Generation
This is where tools like SlideToAnki come in. The idea is simple: upload your lecture file, and AI handles the card creation.
Here's what the process looks like:
- Upload your PDF or PowerPoint — drag and drop, nothing to configure
- AI analyzes every slide — both the text content and any diagrams or figures
- Cards are generated automatically — cloze deletions for factual content, Q&A for conceptual questions, and image occlusion for labeled diagrams
- Review and edit — you see all the cards before exporting, and can edit, delete, or reorder anything
- Export as .apkg — import directly into Anki
The entire process takes about 5 minutes for a typical lecture, compared to 2-3 hours manually.
Why AI Card Generation Actually Works Now
Earlier attempts at auto-generating flashcards were pretty bad. They'd pull random sentences from slides and turn them into poorly formatted cards. Modern AI (large language models specifically) are different because they can:
- Understand context — they know that "EF < 40%" is about heart failure, not just a random number
- Pick the right card type — cloze for facts, Q&A for reasoning, image occlusion for diagrams
- Write natural cloze deletions — not just hiding random words, but hiding the testable part
- Identify what matters — skip slide titles, housekeeping content, and trivial information
- Analyze diagrams — detect labels on anatomical diagrams and create occlusion cards automatically
This last point is particularly important. Image occlusion cards are incredibly effective for anatomy, pathology, and any visual content — but they're the most time-consuming to make by hand. Having AI detect labels and generate occlusion masks automatically eliminates one of the biggest bottlenecks.
Practical Tips for Either Method
Whether you make cards manually or use AI assistance, these principles apply:
Review cards the same day. The first review cements the memory. If you generate cards from a morning lecture, review them that evening.
Don't be afraid to delete cards. Not every slide contains testable information. A card about your professor's research interests probably isn't showing up on Step 1.
Add personal context. After generating cards (manually or with AI), add mnemonics, personal connections, or hints that make sense to you. This is what makes your cards better than a pre-made deck.
Tag consistently. Whether it's by lecture, organ system, or exam block — consistent tagging makes your life easier when you need to do targeted review.
Supplement, don't replace. If you use AnKing for boards, your lecture cards fill the gaps. They're for class-specific content, your professor's emphasis, and material that isn't in the pre-made decks.
The Bottom Line
Making Anki cards from lectures doesn't have to be a 3-hour chore after every class. The manual approach works — millions of students have used it successfully — but it's a massive time investment that adds up fast over a semester.
AI-powered tools like SlideToAnki can compress that process from hours to minutes while maintaining card quality. You still review and edit the output, so you're not giving up control — you're just eliminating the mechanical part of card creation.
The time you save is time you can spend actually reviewing cards, which is where the real learning happens.