Opportunities for Artificial Intelligence in Advancing Precision Medicine.

Area of Science:

• Biomedical Informatics
• Computational Biology
• Digital Health

Background:

• High-throughput technologies generate vast amounts of complex biomedical data, including genomic sequences, medical images, and drug screens.
• Multi-omics data presents opportunities for data-driven insights and automated disease classification.
• Understanding healthy baselines and disease signatures is crucial for advancing precision medicine.

Purpose of the Study:

• To critically evaluate the future potential of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in precision medicine.
• To showcase progress in ML within digital health.
• To identify essential prerequisites for AI and ML in precision health.

Main Methods:

• Review of state-of-the-art applications of ML and DL in biomedicine.
• Analysis of big data challenges in systems biology and biotechnology.
• Evaluation of AI's role in enabling novel therapies.

Main Results:

• Deep neural networks excel in digital image recognition, single-cell clustering, and virtual drug screening.
• ML demonstrates broad applicability and power in analyzing complex biomedical datasets.
• AI and systems biology are addressing big data challenges.

Conclusions:

• AI and systems biology are key to harnessing big data for precision medicine.
• Novel biotechnology-derived therapies can be facilitated by AI and ML.
• These advancements promise to enhance the implementation of precision medicine approaches.