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Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Updated: Jul 15, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

Artificial Intelligence in Clinical Genetics: Current Applications and Challenges.

Rohit Sadanand1, Neerja Gupta2

  • 1Divison of Medical Genetics, Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India.

Indian Journal of Pediatrics
|July 13, 2026
PubMed
Summary

Artificial intelligence (AI) aids in analyzing complex genomic and phenotypic data for medical genetics. Challenges remain in clinical integration due to data bias, ethics, and system compatibility, but AI tools are poised for wider adoption.

Keywords:
Artificial intelligenceClinical geneticsDeep learningElectronic health recordsNext-generation sequencingPrecision medicineRare disease diagnosisVariant interpretation

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Last Updated: Jul 15, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

Area of Science:

  • Genomics
  • Medical Genetics
  • Bioinformatics

Background:

  • Multiomics, next-generation, and long-read sequencing generate massive datasets, challenging traditional analysis in medical genetics.
  • Interpreting vast amounts of variant and biochemical data requires significant time and expertise.

Purpose of the Study:

  • To explore the role and impact of artificial intelligence (AI) in managing complex genomic and phenotypic data within clinical genetics.
  • To identify challenges and opportunities for integrating AI genomic tools into routine medical practice.

Main Methods:

  • Review of current applications of AI in variant prioritization, interpretation, rare disease screening, and precision medicine.
  • Analysis of barriers to clinical translation, including data representation, ethical considerations, and data governance.

Main Results:

  • AI offers powerful solutions for handling the increasing volume and complexity of genomic and phenotypic data.
  • Current AI tools are often underutilized due to isolation from clinical workflows and information systems.
  • Underrepresentation of diverse populations and ethical concerns hinder widespread clinical adoption.

Conclusions:

  • AI is increasingly influencing medical genetics, particularly in variant interpretation and precision medicine.
  • Integration into routine clinical practice requires addressing data bias, ethical issues, and system interoperability.
  • AI genomic tools are expected to become integral to medical genetics practice beyond specialized settings.