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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Opportunities for Artificial Intelligence in Advancing Precision Medicine.

Fabian V Filipp^1,2

¹Cancer Systems Biology, Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 München, Germany.

Current Genetic Medicine Reports

|December 25, 2019

Summary

Machine learning (ML), deep learning (DL), and artificial intelligence (AI) are revolutionizing precision medicine by analyzing complex biomedical data. These technologies offer new insights into disease and enable advanced, data-driven therapies.

Keywords:

AI DL DNN Deep learning Digital health Digital pathology ML Machine learning Multi-omics Precision medicine Single-cell transcriptomics Spatial transcriptomics Systems biology

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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.