Multi-modal AI in precision medicine: integrating genomics, imaging, and EHR data for clinical insights

  • 0Integral Centre of Excellence for Interdisciplinary Research (ICEIR), Integral University, Lucknow, India.

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Summary

This summary is machine-generated.

Artificial intelligence (AI) integrates multi-modal data for personalized medicine, improving disease detection and treatment. This approach enhances diagnostics and drug development, redefining individualized healthcare delivery.

Area Of Science

  • Biomedical Informatics
  • Computational Biology
  • Digital Health

Background

  • Precision healthcare aims for individualized therapeutic strategies.
  • Artificial intelligence (AI) is central to integrating multi-modal patient data.
  • Heterogeneous data streams include genomic, imaging, and electronic health records (EHR).

Purpose Of The Study

  • To review the transformative potential of multi-modal AI in precision medicine.
  • To emphasize AI's role in multi-omics data integration, predictive modeling, and clinical decision support.
  • To critically evaluate challenges in AI-driven precision healthcare.

Main Methods

  • AI-enabled multi-modal data integration consolidates diverse data streams.
  • Machine learning (ML) and deep learning (DL) algorithms extract complex associations.
  • Review of AI applications in digital health, including biosensors and real-time monitoring.

Main Results

  • AI enhances early disease detection, biomarker discovery, and drug development.
  • Improved diagnostic precision, risk stratification, and patient-specific interventions.
  • AI facilitates continuous refinement of treatment plans through digital health tools.

Conclusions

  • The convergence of AI and multi-modal data is redefining individualized healthcare.
  • AI-driven precision medicine offers significant implications for oncology, neurology, and cardiovascular medicine.
  • Addressing challenges like data interoperability, bias, and privacy is crucial for AI adoption.

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