AI_for_biomedical_students

No-Code AI Tools for Biomedical Scientists

No code tools.

Category 1: Conversational AI (Text-Based)

🎯 Claude (claude.ai) - HIGHLY RECOMMENDED FOR YOUR COURSE

Best for: General purpose, long-form analysis, scientific writing, complex reasoning

Why it’s great for biomedical students:

Excellent at explaining complex biological concepts
Strong at analyzing scientific literature
Can process PDFs and images (papers, protocols, microscopy images)
Artifacts feature for creating visualizations and documents
Good safety guardrails and honest about limitations

Teaching Use Cases:

Upload a paper PDF and ask for summaries
Explain complex mechanisms (e.g., “Explain the complement cascade”)
Draft methods sections from experimental notes
Troubleshoot experimental problems
Generate practice questions for studying

Demo Exercise: “Upload a paper from your field and ask Claude to identify the gap in knowledge the authors are addressing”

🎯 ChatGPT (chat.openai.com) - ALSO RECOMMENDED

Best for: Quick questions, code generation, brainstorming, web search

Why it’s great for biomedical students:

Free tier available
Voice mode for hands-free interaction
GPT-4 has web search capabilities (paid)
Canvas feature for collaborative document editing
Large user community with lots of shared prompts

Teaching Use Cases:

Quick concept explanations during study sessions
Generate quiz questions from lecture notes
Create study schedules and learning plans
Explain statistical tests in simple terms
Generate Python/R code for data analysis

Demo Exercise: “Ask ChatGPT to create a 5-question quiz on cell cycle regulation, then evaluate the quality of the questions”

Google Gemini (gemini.google.com)

Best for: Integration with Google Workspace, real-time information

Why it’s useful:

Connects to Google Drive, Docs, Gmail
Can access recent information via Google Search
Multimodal (text, images, video)
Free tier available

Teaching Use Cases:

Summarize emails from collaborators
Search your Google Drive for specific protocols
Analyze data in Google Sheets
Current event updates in biomedical research

Perplexity AI (perplexity.ai)

Best for: Research, fact-checking, sourced answers

Why it’s valuable:

Provides citations for all claims
Good for literature searching
Pro version has academic search mode
Shows multiple perspectives

Teaching Use Cases:

Finding recent papers on specific topics
Fact-checking claims from AI responses
Getting overview of research areas
Following citation trails

Demo Exercise: “Research the current state of mRNA vaccine technology and compare Perplexity’s cited sources with uncited AI responses”

Category 2: Literature & Research Management

🎯 Elicit (elicit.com) - ESSENTIAL FOR BIOMEDICAL RESEARCH

Best for: Literature review, systematic reviews, finding relevant papers

Features:

AI-powered paper search across 125M+ papers
Automatically extracts key data from papers
Creates summary tables of findings
Identifies methodologies and sample sizes
Finds similar papers

Teaching Use Cases:

Systematic literature reviews
Finding papers for journal clubs
Identifying common methodologies in a field
Extracting data for meta-analyses
Finding research gaps

Demo Exercise: “Use Elicit to find 10 papers on CRISPR base editing and create a comparison table of the editing efficiencies reported”

Cost: Free tier available, paid for advanced features (~$10/month)

Consensus (consensus.app)

Best for: Finding scientific consensus, evidence-based answers

Features:

Searches 200M+ scientific papers
Synthesizes findings across studies
Shows level of consensus on topics
Evidence-based meter for claims
Direct paper citations

Teaching Use Cases:

Quickly understand “what does the research say about X?”
Literature reviews for grant proposals
Fact-checking health claims
Understanding controversial topics

Demo Exercise: “Is there scientific consensus on the gut-brain axis affecting mental health? Use Consensus to find out.”

Cost: Free tier available

SciSpace (typeset.io)

Best for: Understanding difficult papers, explaining concepts

Features:

Chrome extension to highlight and explain text in papers
Ask questions about specific sections
Explains math and methods
Summarizes papers
Literature review tools

Teaching Use Cases:

Reading complex papers for the first time
Understanding statistical analyses in papers
Journal club preparation
Learning new techniques from methods sections

Demo Exercise: “Find a paper outside your expertise area, use SciSpace to explain the key methodology”

Cost: Free tier available

Scholarcy (scholarcy.com)

Best for: Article summarization, flashcard creation

Features:

Creates structured summaries of papers
Extracts key findings, figures, tables
Generates flashcards for studying
Browser extension available
Creates literature review matrices

Teaching Use Cases:

Quick paper overviews before detailed reading
Creating study materials from papers
Building summary tables for thesis literature reviews

Cost: Free trial, then ~$5/month

Category 3: Image Analysis & Scientific Visualization

🎯 ChatGPT/Claude with Vision

Best for: Analyzing microscopy images, interpreting figures

Features:

Upload images for analysis
Describe what’s in scientific images
Suggest quantification approaches
Identify potential artifacts
Compare before/after images

Teaching Use Cases:

“What cell type is this?” (from microscopy)
“Count the colonies in this plate image”
“What’s wrong with this Western blot?”
Interpreting complex figure panels from papers
Getting suggestions for image analysis approaches

Demo Exercise: “Upload a microscopy image and ask the AI to describe what it sees and suggest quantification methods”

Cost: ChatGPT Plus ($20/month), Claude Pro ($20/month), or free tiers with limitations

Napkin AI (napkin.ai)

Best for: Creating visual diagrams from text

Features:

Converts text concepts into diagrams
Flowcharts, timelines, process diagrams
No design skills needed
Export for presentations

Teaching Use Cases:

Visualizing experimental workflows
Creating pathway diagrams for presentations
Timeline of disease progression
Study guide visuals

Demo Exercise: “Describe a signaling pathway in text, then use Napkin to visualize it”

Cost: Free tier available

Gamma (gamma.app)

Best for: Creating presentations and documents

Features:

AI-generated presentations from prompts
Scientific themes available
Auto-formats complex content
Exports to PowerPoint
Can include AI-generated images

Teaching Use Cases:

Journal club presentations
Conference poster drafts
Study guides
Grant proposal outlines

Demo Exercise: “Create a 5-slide presentation on PCR for undergraduates”

Cost: Free tier (limited), ~$8/month

Category 4: Data Analysis & Statistics

🎯 Julius AI (julius.ai)

Best for: Data analysis, visualization, statistics - NO CODING REQUIRED

Features:

Upload CSV, Excel files
Ask questions in plain English
Auto-generates graphs and charts
Performs statistical tests
Explains analysis steps
Python code is generated but you don’t need to write it

Teaching Use Cases:

Analyzing qPCR data
Creating publication-quality graphs
Running t-tests, ANOVA without coding
Exploring patterns in datasets
Learning what statistical tests to use

Demo Exercise: “Upload sample qPCR data and ask: ‘Is there a significant difference between the control and treatment groups? Show me a graph.’”

Cost: Free tier available, Pro ~$20/month

DataChat (datachat.ai)

Best for: Conversational data analysis

Features:

Chat-based data analysis
No SQL or coding needed
Data cleaning and transformation
Statistical analysis
Visualization

Teaching Use Cases:

Cleaning messy datasets
Exploratory data analysis
Creating multiple visualizations quickly
Learning data analysis concepts

Cost: Free trial, then enterprise pricing (may be expensive for students)

Akkio (akkio.com)

Best for: Predictive modeling without code

Features:

Upload data, AI builds models
Predicts outcomes
Identifies important variables
Explains predictions

Teaching Use Cases:

Understanding machine learning concepts
Predicting experimental outcomes
Classification problems (disease vs. healthy)

Cost: Free trial, paid tiers

Category 5: Writing & Communication

🎯 Grammarly (grammarly.com) with AI

Best for: Scientific writing improvement

Features:

Grammar and clarity checking
Tone adjustment
AI writing assistance
Citation consistency checking
Plagiarism detection (paid)

Teaching Use Cases:

Improving manuscript drafts
Ensuring clear scientific communication
Checking grant proposals
Email writing to PIs/collaborators

Cost: Free tier available, Premium ~$12/month

Wordtune (wordtune.com)

Best for: Rewriting and improving sentences

Features:

Suggests better phrasings
Expands or shortens text
Changes tone (formal/casual)
AI writing from prompts

Teaching Use Cases:

Making results sections clearer
Condensing methods sections
Improving abstract clarity
Making technical writing more accessible

Cost: Free tier available, Premium ~$10/month

QuillBot (quillbot.com)

Best for: Paraphrasing and summarizing

Features:

Paraphrasing tool (BE CAREFUL: use ethically!)
Summarization
Grammar checking
Citation generator

Teaching Use Cases:

Summarizing long papers
Understanding complex sentences
Grammar practice
(NOT for copying other’s work!)

Cost: Free tier available, Premium ~$8/month

Category 6: Protein & Molecular Biology

🎯 AlphaFold Server (alphafoldserver.com)

Best for: Protein structure prediction

Features:

Predicts 3D protein structures
No coding required
Just input amino acid sequence
Visualizes structures
Highly accurate

Teaching Use Cases:

Understanding protein structure
Predicting mutations’ effects
Structural biology assignments
Hypothesis generation for protein function

Demo Exercise: “Input a protein sequence and explore the predicted structure. How might this structure relate to function?”

Cost: Free for academic use

ESMFold (esmatlas.com)

Best for: Fast protein structure prediction

Features:

Similar to AlphaFold
Faster predictions
Large protein database
Browser-based

Teaching Use Cases:

Quick structure checks
Comparing protein families
Teaching protein structure concepts

Cost: Free

Category 7: Specialized Biomedical AI

PathAI (pathai.com) - Enterprise/Clinical

Best for: Pathology image analysis (mostly clinical settings)

Note: Primarily for clinical labs, but good to know about

BenchSci (benchsci.com)

Best for: Antibody and reagent selection

Features:

AI-powered antibody search
Evidence from publications
Experiment recommendations
Protocol suggestions

Teaching Use Cases:

Selecting antibodies for experiments
Finding validated reagents
Understanding antibody specificity

Cost: Free tier for academics

Benchling AI (within Benchling platform)

Best for: Electronic lab notebook with AI features

Features:

AI writing assistance for protocols
Experiment suggestions
Data analysis
Molecular biology tools

Cost: Free for academics

Category 8: Coding Assistants (Minimal Coding Required)

GitHub Copilot (github.com/features/copilot)

Best for: Learning to code with AI help

Features:

AI suggests code as you type
Explains code
Debugs errors
Works in many programming languages

Teaching Use Cases:

Learning Python/R with AI assistance
Understanding bioinformatics scripts
Debugging analysis code
Learning by example

Cost: Free for students with GitHub Education

Demo Exercise: “Start writing a Python script to analyze data, let Copilot suggest the code, then ask it to explain each line”

Replit AI (replit.com)

Best for: Learning to code in browser, no installation

Features:

Browser-based coding environment
AI code generation and explanation
No setup required
Collaborative coding

Teaching Use Cases:

First Python/R scripts
Quick data analysis
Sharing code with classmates
Learning syntax

Cost: Free tier available

Recommended Tool Stack for Your Course

Essential Tier (Free/Cheap - Everyone Should Use)

Claude or ChatGPT - Main AI assistant
Elicit - Literature review
AlphaFold Server - Protein structures
Julius AI - Data analysis
ChatGPT/Claude Vision - Image analysis

Enhanced Tier (If budget allows ~$20-30/month)

Claude Pro or ChatGPT Plus - Better image analysis, longer responses
Grammarly Premium - Writing improvement
Consensus - Research synthesis

Specialized Tier (Field-specific)

BenchSci - If doing wet lab work
GitHub Copilot - If coding is important

Teaching Integration Strategies

Week 1: Introduction Session

Tools to introduce:

ChatGPT or Claude (pick one as primary)
Show both, let students choose preference
Demo with real biomedical example

Exercise: Each student asks the same question to both AIs and compares responses

Week 2: Literature Management

Tools to introduce:

Elicit
Consensus or SciSpace
Perplexity for fact-checking

Exercise: Conduct a mini literature review on a topic, comparing manual vs. AI-assisted approaches

Week 3: Data Analysis

Tools to introduce:

Julius AI
ChatGPT for simple code generation

Exercise: Analyze provided dataset and create publication-quality figure

Week 4: Writing & Communication

Tools to introduce:

Grammarly or Wordtune
Use ChatGPT/Claude for drafting

Exercise: Write and revise an abstract using AI assistance

Hands-On Teaching Activities

Activity 1: Tool Comparison Race (20 minutes)

Divide class into groups
Each group uses different tool to answer same question
Compare: speed, accuracy, ease of use, cost
Discuss: When would you use each?

Activity 2: AI Fact-Check Challenge (30 minutes)

Instructor provides AI-generated “facts” about biomedical topics
Some accurate, some hallucinations
Students use various tools to verify
Teaches critical evaluation

Activity 3: Build Your AI Workflow (45 minutes)

Students design AI-assisted workflow for their own research
Map out which tools for which steps
Share with class
Get feedback

Activity 4: Tool Limitations Exploration (30 minutes)

Deliberately find tool limitations
What questions do they fail on?
What errors do they make?
Builds healthy skepticism

Quick Reference Chart for Students

Task	Best No-Code Tool	Alternative	Cost
General questions	Claude / ChatGPT	Gemini	Free/Premium
Literature review	Elicit	Consensus	Free tier
Reading papers	SciSpace	Scholarcy	Free tier
Data analysis	Julius AI	ChatGPT	Free tier
Making graphs	Julius AI	ChatGPT Code Interpreter	Free tier
Image analysis	ChatGPT/Claude Vision	-	Premium
Writing help	Grammarly	Wordtune	Free tier
Presentations	Gamma	Google Slides + AI	Free tier
Protein structures	AlphaFold Server	ESMFold	Free
Finding antibodies	BenchSci	Manual search	Free academic
Learning to code	Replit AI	GitHub Copilot	Free tier

Safety & Ethics Reminders

When teaching these tools, emphasize:

✅ Do:

Use for learning and understanding
Verify all factual claims
Cite when AI significantly contributed
Use to augment your skills
Explore and experiment

❌ Don’t:

Upload confidential patient data
Trust without verification
Submit AI text as entirely your own without review
Use for plagiarism or cheating
Share proprietary research data

Assessment Ideas Using These Tools

Assignment 1: Tool Review

Students test 3 tools, write brief review:

What it does well
What it does poorly
Best use cases
Cost-benefit analysis

Assignment 2: AI-Assisted Mini Project

Complete a small research task using AI tools:

Literature review (Elicit)
Data analysis (Julius)
Write results (Claude/ChatGPT)
Document what AI did vs. what you did

Assignment 3: Teaching Guide

Create a guide teaching another student how to use one tool for a specific biomedical task

Troubleshooting Common Issues

“The tool is too slow/won’t work”

Check internet connection
Try different browser
Some tools have usage limits - wait or upgrade
Server might be down - try alternative tool

“I don’t understand the AI’s answer”

Ask it to explain more simply
Request step-by-step breakdown
Ask for examples
Try different phrasing of your question

“The AI is giving wrong information”

This happens! Always verify important claims
Try multiple AI tools for comparison
Check primary sources
Use tools like Consensus that cite sources

“I can’t afford the premium tools”

Many tools have free academic tiers
Most free tiers are sufficient for learning
Share accounts where allowed
Focus on free alternatives listed

Staying Updated

AI tools change rapidly. Teach students to:

Follow AI newsletters (e.g., TLDR AI)
Join biomedical AI communities
Check for new features quarterly
Share discoveries with peers
Build adaptability skills

Resources for instructors:

r/bioinformatics on Reddit
X (Twitter) hashtags: #ScienceAI #BioML
Newsletters: The Batch (deeplearning.ai)

Final Tips for Teaching

Start simple - Don’t overwhelm with too many tools at once
Make it relevant - Use examples from their actual coursework/research
Encourage experimentation - There’s no “wrong” way to prompt
Build critical thinking - Tools are assistants, not oracles
Share failures - Show when AI gets things wrong
Celebrate creativity - Students will find uses you didn’t think of
Update regularly - New tools emerge constantly

Remember: The goal is not to master every tool, but to develop the mindset and skills to effectively use AI tools throughout their careers.

Additional Resources

AI Tool Directories:
- futuretools.io (updated daily)
- theresanaiforthat.com
Academic AI Guides:
- Many universities now publish AI usage guidelines
- Check your institution’s policies
Practice Datasets:
- Kaggle biomedical datasets
- UCI Machine Learning Repository
- Gene Expression Omnibus (GEO)

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