Physics

Translational research

Pathology

Translational medicine

Bioinformatics

Biology

Data mining

Machine learning

Precision medicine

Artificial intelligence

Programming language

Data science

Scale (ratio)

Computer science

Ambiguity

Translational science

Quantum mechanics

Drug discovery

Big data

Medicine

EBioMedicine

Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. However, ML technologies do not come without their limitations and shortcomings. Current technical limitations and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.

Looking beyond the hype: Applied AI and machine learning in translational medicine