Anki for medical students: how to use it, when to add AI, and when to switch
By Roupen Odabashian, MD, FRCPC · Published · Last updated · 17 min read
How we write and check articles: Editorial Policy.
Disclosure: Alma publishes this article and makes one of the study tools it discusses.
If you are in medical school in 2026, Anki is still the default flashcard app for memorizing huge amounts of detail, especially with community decks like AnKing.[1][2] Where it breaks down is card-creation time, fatigue, and keeping context. AI card makers and newer spaced-repetition tools help, but they do not replace understanding, practice questions, or clinical reasoning.[3]
In this guide we will lay out how to set up Anki for medical school, the add-ons and workflows that matter, how AI Anki card makers compare, and when an alternative spaced-repetition app is a better fit. Alma makes an AI-first study tool with flashcards and spaced repetition, but Anki, AI generators, and Alma solve different parts of the same problem. We will be explicit where another tool is the better pick, and we encourage you to read how we work with evidence in our editorial standards.
How should medical students use Anki without burning out?
Anki works best in med school when it is the last step in a study pipeline, not the first: learn the material, synthesize it in your own words, then make a small number of high-quality cards you can realistically review every day.[4][3] Using Anki for memory-heavy details and practice testing, while keeping card numbers manageable, beats trying to encode every sentence.
Several long-form med student guides stress that Anki should come after initial learning, not replace it.[4][3] One third-year osteopathic medical student describes a workflow of listening actively to lecture, rewriting concepts in their own words, filling gaps with supplementary resources, and only then making cards.[4] Another guide summarises the same idea: use Anki to review high-volume material with active recall and spaced repetition, but not as a replacement for understanding or clinical reasoning.[3]
This aligns with evidence on test-enhanced learning: research on retrieval practice shows that recalling information produces better long-term retention than passive rereading.[3] Spaced review, rather than cramming, fights the forgetting curve documented in classic and more recent work.[1] In practice, that means short, repeated card sessions are worth more than marathon last-minute reviews.
Where students get into trouble is volume. One guide bluntly calls the extremes — making a card for everything or relying only on giant pre-made decks — unworkable for most people.[4] The same author argues that a deck of 200 excellent cards you can actually clear daily beats 800 mediocre ones you abandon.[4] Another estimate suggests many beginners do well starting with 20–40 new cards per day and a max of 150–300 reviews, adapting upward only when the workload is stable.[3]
| Setting | Suggested range | Notes |
|---|---|---|
| New cards per day | 20–40 | Some sources suggest starting in this range and only increasing new cards if your review load feels manageable.[3] |
| Maximum reviews per day | 150–300 | Guides for medical students often advise setting a daily review cap in this range to keep workloads sustainable.[3] |
| Learning steps | 1 minute, 10 minutes | Beginner-focused advice commonly recommends short early intervals like 1 minute and 10 minutes for new cards.[3] |
Good card style also matters. Multiple guides converge on “one concept per card, in your own words,” with cloze deletions for quick buzzword-to-answer mapping and cards built from missed questions that capture your reasoning errors.[4][3] This keeps cards atomic and testable, and ties them directly to how you think — which matters more than packing maximal facts into each one.
For day-to-day use, Anki’s core strengths are that it schedules reviews based on how well you recall each card and lets you tune the timing.[5] Newer versions offer the FSRS scheduling algorithm, which can time reviews more efficiently than older defaults, reducing review load for many students.[3][6] But the algorithm only helps if you can face your queue daily. That is where AI and tools like Alma come in: not to change the retrieval practice itself, but to shrink the time you spend building the pipeline that feeds into Anki.
What is the best way to make Anki cards from lectures and resources?
The most reliable way to make Anki cards in med school is to process the lecture first, then write concise, single-concept cards in your own words, focusing on high-yield facts and missed questions.[4][3] AI tools can speed this up, but you still need to edit cards for clarity and relevance to your curriculum.
One detailed workflow from a third-year DO student looks like this: listen to lecture with minimal notes, rewrite the material in your own words, fill gaps with supplementary resources, and only then make cards.[4] They emphasize that making cards during lecture often devolves into copying text, whereas making them after this “double processing” step turns understanding into testable prompts.[4]
Another guide suggests a hybrid approach for board prep: use a solid pre-made “board deck” as the base, then add 5–15 lecture-specific cards per topic based on repeated emphasis by your professor and concepts you miss on self-testing.[3] This keeps your deck manageable while still tying it to what your school actually tests.
Time cost is real. One comparison of flashcard tools notes that many students report two to three minutes per well-made cloze card, so even 40 new gap cards in a week can mean one to two hours of pure typing.[1] Another article on Anki alternatives points out that unless a shared deck covers your subject, you write every card yourself, and turning one chapter into good cards can take longer than reading it.[7] This card-creation bottleneck is exactly what modern AI card makers are trying to ease.
How does Alma help you turn your own notes into spaced-repetition flashcards?
You can read more about these tools on Alma’s learning tools page or check current plan details on the pricing page as of October 2026.
Which Anki add-ons actually matter for medical students?
Most medical students do not necessarily need dozens of Anki add-ons; a focused set that improves card formats, handles images, and connects to medical references is often enough.[6][8]
Several 2026 roundups of “best Anki add-ons for med students” converge on the idea that Anki’s default interface is powerful but bare-bones, and that only a subset of plugins are essential.[6][9] One guide notes that critical features for med students — especially around card creation and visualization — often require add-ons or external tools.[6] Another, focused on medical students, describes testing dozens of add-ons but recommends only the ten that consistently make the biggest difference.[9]
Image-based learning is a prime example. Image Occlusion, which lets you hide labels on diagrams and test them one by one, is widely considered mandatory for anatomy, radiology, and histology.[8] It used to require a popular plugin, but since Anki 23.10 it is a built-in note type: you choose Image Occlusion on the Add screen and draw rectangles over labels you want to hide.[8]
For text-heavy decks, standardized note types matter. The note types used by the AnKing deck — one of the most popular pre-made USMLE decks — rely on a plugin that provides specific formatting, cloze behaviors, and toggleable extra fields.[6][8] Guides aimed at board-style studying describe that plugin as effectively required if you plan to use these decks correctly.[6][8]
Other add-ons focus on workflow and focus rather than content. Tools like Review Heatmap visualize your consistency over time, while timing add-ons such as Speed Focus Mode limit the time spent on simple cards to fight “Anki fatigue.”[6][8] These do not change what you learn, but they help keep your reviews efficient and sustainable.
| Purpose | Tool | What it adds |
|---|---|---|
| Anatomy and images | Image Occlusion (built-in or Image Occlusion Enhanced add-on) | Lets you take a diagram, hide specific labels with boxes, and create multiple cards from a single image.[6][8] |
| USMLE-style decks | AnKing note types plugin | Provides standardized note types with fields designed for popular USMLE decks, including cloze deletions and toggleable extra fields with clean formatting.[6][8] |
| Scheduling efficiency | FSRS scheduler (built-in) | A modern scheduling algorithm built into recent Anki versions that aims to time reviews more efficiently than older defaults.[3][6][8] |
| Motivation and pacing | Review Heatmap, Speed Focus Mode | Review Heatmap tracks your daily review streak and forecast, while timing-focused add-ons can limit how long you spend on each card.[6][9][8] |
One guide also stresses the importance of limiting add-ons based on your year. Their recommended M1/M2 stack emphasizes built-in Image Occlusion, standardized note types, sticky fields for faster card creation from lecture PDFs, and an integrated medical library add-on (such as AMBOSS) for conceptual gaps.[8] More complex plugin stacks are deferred to later phases, when you are managing mature decks rather than just building them.
Outside Anki itself, other tools integrate via the AnkiConnect API. For example, one app describes using AnkiConnect to send AI-generated cards directly into Anki from its own interface, bypassing manual exports.[9] This theme — external tools for creation, Anki for scheduling — also shows up in AI card makers aimed at medical students, which we will compare next.
How do AI Anki card makers compare for medical school?
AI card makers do not replace learning, but they can slash the time it takes to turn lectures and PDFs into flashcards. Most tools aimed at med students share the same promise: upload your material, get draft cards back, then edit aggressively. The differences are in what inputs they handle and how they connect to Anki.[6][8][1]
Multiple sources describe AI card generation primarily as an answer to the card-authoring bottleneck. One review points out that many students spend minutes per well-made cloze card, so even modest weekly targets can consume hours of typing.[1] Another notes that manually creating cards from a 50-page PDF can take hours and positions AI as a way to bypass the most tedious parts of that phase.[8]
SlideToAnki, for example, lets you upload a lecture PDF or PowerPoint and generates Anki flashcards, including cloze deletions, Q&A cards, and image occlusion cards.[6] It can detect labels on diagrams and build image occlusion cards without you manually drawing boxes.[6] A summary claims this compresses what used to be two to three hours of manual card-making per lecture into a few minutes of setup and later editing.[6]
Other AI-focused tools also frame themselves around this time savings. One article on flashcard apps says that for board content that is already written, Anki with a community deck is the standard, while an AI app is best for turning your own lectures and PDFs into cards quickly.[1] Another comparison of Anki alternatives describes tools that can generate cards from notes or PDFs and either export them directly into Anki formats or preserve Anki review schedules on import.[8][7]
The trade-offs are predictable. AI saves you typing and can surface structure you might miss, especially on complex slides, but it cannot know exactly how your exam will phrase questions. Guides aimed at med students repeatedly warn that card quality still depends heavily on how you structure your notes and how aggressively you edit auto-generated cards.[6][8] Your job is to turn AI drafts into clean, single-concept prompts in your own words.
When is an Anki alternative better for med school, and where does Alma fit?
Anki remains the right choice if you rely heavily on shared decks, add-ons, or desktop study.[7] An alternative makes more sense if your main pain points are card creation overhead, fragmented context between notes and cards, or a desire for built-in AI workflows instead of plugin stacks.[7][2] Alma belongs in this second category: a “notes-first” study environment with spaced repetition and AI baked in.
Several writers who are enthusiastic about spaced repetition still point out reasons students look beyond Anki. One review notes that the reasons people leave Anki are rarely about the algorithm itself — its spaced-repetition implementation is solid — but about overhead and workflow, such as the time spent authoring cards and managing decks.[7][2] They highlight how burning time on card creation and deck management can turn into its own job, independent of learning.[1][7][2]
Tools like RemNote or Mochi respond by putting note-taking and spaced repetition in one place, often with AI that converts notes or PDFs into flashcards and preserves card schedules when importing Anki decks.[8][7] Others, like Prismo, position themselves explicitly as best for people who dread making cards, letting you write a short summary and having the app handle question generation and scheduling.[7] These apps still test you and space reviews — the two pillars of effective spaced repetition — but change everything around that core.[7]
| Your situation | Leaning toward Anki | Leaning toward an alternative or Alma |
|---|---|---|
| Depend on shared USMLE decks and add-ons | Yes — Anki is default for AnKing and plugins | Only if you are willing to rebuild decks |
| Dread card creation and plugin setup | Only with AI helpers feeding Anki | Yes — tools with built-in AI pipelines |
| Want notes, diagrams, and cards in one place | Possible, but often clunky | Yes — note-first SRS tools including Alma |
How does Alma handle podcasts, videos, and “standardized patients” compared with Anki?
On the audio side, Alma’s Podcast Radio Station is a library of existing shows, organized by topic, that you can listen to inside Alma. Notes you take while an episode plays go straight into the Notebook, where you can turn them into mind maps, diagrams, or flashcards later. Alma is not turning your own notes into podcast audio, but it tries to close the gap between passive listening and active study.
For video, Alma’s Mind Palace feature turns your lecture content into a narrated video in several styles (with the current styles confirmed by the owner before release). This is built to reinforce your understanding visually and verbally, using the material you already uploaded or wrote, and it links back into the same Notebook and Knowledge Graph ecosystem.
Alma also includes standardized practice patients: OSCE-style stations you can work through by presentation or specialty to rehearse history taking and clinical reasoning. Alongside them, Alma's broader platform includes a Knowledge Graph to map concepts, Handouts to summarize them, and Games that quiz recall from your own material in a quiz-show format. These are designed to move you from simple recall toward application, using the same underlying notes and uploads that feed your flashcards.
If your priority is pure flashcard efficiency on existing board-style content, Anki plus a mature community deck still makes sense.[1][2] If you want a single environment where lectures, podcasts, video explanations, diagrams, and spaced-repetition flashcards all hang off the same notes — and you are comfortable verifying AI-generated content against trusted resources — Alma is closer to that vision.
How should AI and Anki fit into an overall med school study plan?
Anki and AI are tools inside your study plan, not the plan itself. The evidence favours practice testing and spaced review; your workflow should focus on getting those two things consistently, with as little friction as possible.[3][1] Whether you stick with pure Anki, add AI-based card makers, or move into a tool like Alma comes down to where you are losing time or motivation.
Research summaries on retrieval practice show that active recall outperforms passive rereading for long-term retention.[3] Large spacing experiments, and broader reviews of study techniques, consistently find that distributing review over time — rather than cramming — is one of the few high-utility strategies across learners.[1] Anki’s algorithms are an implementation of that evidence: they test you and schedule future reviews by difficulty.[5][2]
For a practical weekly plan, one guide suggests a seven-day on-ramp for beginners: install and sync Anki, enable FSRS if your version supports it, set reasonable limits for new cards and reviews, experiment with a small deck, then tag and suspend low-yield cards while you test your workload tolerance.[3] The point is to end the week with a sustainable queue, not a heroic spike in total reviews.
Layering AI and Alma into this is about removing drag. If you find yourself spending hours every week typing cards from PDFs, an AI card maker that converts slides into draft Anki cards can win back that time — as long as you still review and edit the outputs.[6][8][1] If your biggest issue is scattered context — lecture notes in one app, flashcards in another, mnemonics in a third — working in Alma’s Notebook, using mind maps, diagrams, and quizzes from your own uploads, can consolidate those steps before you even think about card scheduling.
None of these tools replace primary resources, official exam content, or clinical guidelines. When you use AI to generate explanations or flashcards, treat them as drafts. Verify key facts against your lecture notes, trusted textbooks, question banks, or your institution’s materials before you rely on them. AI is excellent for generating practice material, building visualizations, and scheduling reviews, but it is not a clinical decision tool and not a shortcut around professional judgment.
Frequently asked questions
How many Anki cards per day is realistic in medical school?
Guides aimed at med students suggest starting with around 20–40 new cards per day, with a cap of roughly 150–300 reviews.[3] Some students later increase to 50 new cards once their review load feels stable.[3] The key is consistency: a smaller number you clear daily beats an impressive count that leads to burnout.
Should I build my own Anki deck or just use a pre-made one?
There is no universal answer. One experienced student argues that “make a card for everything” and “only use pre-made decks” both fail most people.[4] A hybrid approach — where you use a strong board deck for core facts, then add a small number of lecture-specific cards and cards from missed questions — is often recommended as a way to balance efficiency with relevance.[4][3]
Can AI-generated cards replace my own Anki cards?
AI card makers are best viewed as accelerators, not replacements. Reviews note that AI drastically cuts typing time from PDFs and slides,[6][8][1] but card quality still depends on how you edit and curate the results. You should treat AI-generated cards as drafts, rewrite them in your own words where needed, and verify key facts against trusted resources.
Where does Alma fit if I already use Anki?
If you are happy inside pure Anki, you might use Alma selectively: upload lectures to generate quizzes, use the Notebook to build diagrams and mind maps, then turn the cleaned-up notes into Anki cards. If you want a more integrated environment, you can keep flashcards inside Alma, scheduled with FSRS, and use its Podcast Radio Station, Mind Palace, Knowledge Graph, Handouts, and Games to connect passive learning to active recall and application.
References
- Best Flashcard Apps for Medical and Nursing School (2026)
- The best Anki alternatives for medical students in 2026 — Fluera
- How to Use Anki for Medical School in 7 Smart, Beginner-Friendly Steps
- How to Use Anki in Medical School (The Way That Actually Works) — Kameron Keoho
- Anki - powerful, intelligent flashcards
- Best Anki Add-ons and Tools for Medical Students in 2026 | SlideToAnki
- The 8 Best Anki Alternatives (2026), Compared
- Best Anki Add-ons for Med School: The Complete 2026 Guide
- The 10 Best Anki Add-ons for Medical Students in 2026 | Mindgrip