Practical Guide to AI for Cancer Researchers: Image Analysis, NLP, and Drug Discovery
This Nature Reviews Cancer guide targets non-computational cancer researchers, conveying general principles of AI for image analysis, natural language processing and drug discovery. The authors demonstrate how off-the-shelf tools can boost research productivity in daily workflows and extract hidden information from existing datasets. For laboratory scientists and pathologists, this provides an accessible entry point to incorporating AI-based analysis into translational research pipelines.
The original study
A guide to artificial intelligence for cancer researchers.
- Authors
- Perez-Lopez R, Ghaffari Laleh N, Mahmood F, Kather JN
- Journal
- Nature reviews. Cancer
- Type
- Journal Article, Review
- PMID
- 38755439
Original abstract
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work.