AI & Big Data in Medicine
AI in Data Analysis
Artificial intelligence is transforming how we analyze medical data. AI models can process thousands of patient records, imaging scans, and genomic sequences to predict disease risk, treatment efficacy, and drug interactions. Algorithms are now capable of outperforming radiologists in detecting certain cancers.
Key Applications:
- Predicting disease risk
- Identifying genetic biomarkers
- Assessing drug effectiveness
- Analyzing medical images and scans
- Enhancing the accuracy of clinical decision support systems
Drug Discovery
AI accelerates drug development by predicting molecule-target interactions and simulating drug trials. Companies like DeepMind (AlphaFold) and Insilico Medicine are revolutionizing how we discover and test new drugs using deep learning models.
Examples of Drugs Discovered with AI:
- DSP-1181: The first AI-designed drug to enter human clinical trials. Developed by Exscientia and Sumitomo Dainippon Pharma, it targets obsessive-compulsive disorder (OCD).
- ABBV-CLS-484: An AI-guided small molecule immuno-oncology drug co-developed by Calico Life Sciences and AbbVie.
- INS018_055: Discovered by Insilico Medicine, this fibrosis-targeting compound was rapidly advanced into clinical trials using AI-driven generative chemistry.
CRISPR & Personalized Me
CRISPR & Personalized Me
Machine learning is used to tailor treatment plans based on genetic makeup. CRISPR-Cas9 gene-editing tools are enhanced by AI algorithms that increase targeting precision and reduce off-target effects. This makes personalized, genetically-informed therapies more effective and accessible.
CRISPR-AI Workflow Diagram:
- Patient genome sequenced
- ML model identifies mutation
- AI optimizes CRISPR target site
- Gene edited with precision
- Post-edit data used to refine future models