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
The time depends on the lecture's length, how much you already understand, and how many diagrams need separate work. Reading, rewriting, and checking every card can become a substantial part of your study session.
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.
- Only re-reading slides: Familiar-looking material can feel learned before you can recall it without looking. Test yourself on the key ideas before deciding you know them.
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.
Worked Example: Turn a Lecture Slide into Answerable Cards
This is an illustrative teaching example, not an actual SlideToAnki export. Use it to judge a draft whether you make cards yourself or generate them with AI.
Suppose a slide titled "Cell cycle overview" says:
- DNA replication occurs during S phase.
- M phase includes mitosis and cytokinesis.
- The next lecture will cover cell-cycle checkpoints.
Start with the learning objective: Recall which phase contains each process. A prompt such as "Describe the cell cycle" asks for more than this slide supports.
Make one focused cloze:
DNA replication occurs during {{c1::S phase}} of the cell cycle.Make one focused Q&A:
- Front: Which two processes are included in M phase of the cell cycle?
- Back: Mitosis and cytokinesis.
Leave out the housekeeping: "What will the next lecture cover?" does not help you recall the cell-cycle concepts. Reject that card unless the scheduling detail is genuinely something you need to learn.
Check the scope: If a draft lists checkpoint proteins that are not on this slide, check the rest of the lecture before keeping it. Correct background knowledge can still be the wrong material for this lecture deck.
Plan the import check: In Anki, the cloze prompt should hide "S phase," and the Q&A answer should contain only the two requested processes. Add a tag such as lecture::cell-cycle so the cards remain easy to find alongside the rest of your course.
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 a workflow you can follow with your next lecture:
- Sign in and choose your file. SlideToAnki accepts PDF, PPT, and PPTX files up to 50 MB. Start with one lecture so the source material is easy to check.
- Let the cards generate. The draft can include cloze deletions, Q&A, and image occlusion. Generation time varies with the file and its diagrams; allow time to inspect the result as well.
- Review against the lecture. Check the prompt and full answer, use the available source context, and compare uncertain details with your original slides. Edit unclear wording and reject duplicates or unsupported cards.
- Finish the deck deliberately. If you choose Keep full deck, unchecked cards are included too. Continue reviewing if you want to inspect every card before export.
- Download the .apkg. Once the deck is ready, choose Download .apkg, then use File → Import in Anki desktop. Preview a few cards and any diagrams before starting your reviews.
For file-specific preparation and import troubleshooting, follow the PowerPoint-to-Anki walkthrough. Anki's importing guide explains how to open a deck package.
What to Check Before You Keep a Generated Card
AI gives you a draft to work from. Your lecture and learning objectives remain the reference:
- Can you tell what answer is expected? Add a condition or topic to a vague prompt.
- Does the source support the entire answer? Check qualifiers, units, and exceptions, not just the main term.
- Does the card test a useful idea? Remove repeated facts and lecture housekeeping.
- Does the image actually work? Inspect the mask, label, and answer together. Small labels, complex figures, or unsupported image formats may not convert cleanly.
- Will it still make sense next month? Replace phrases like "this process" with the name of the process.
If a figure is missing or unreadable, make that image card manually rather than memorizing an incomplete card. Our image occlusion guide can help with the manual approach.
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 targeted review easier. Use the Anki tags guide to organize your imported notes without creating a separate deck for every topic.
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
Start with one lecture, make the prompts specific, and check the answers against the source. Manual card creation is a good fit for a small set of concepts; AI-assisted drafting can help when the volume of slides is the bottleneck.
The useful outcome is a deck you understand and can keep reviewing. Spend the time you save on checking difficult concepts and practicing recall.