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Issues And Trends In Healthcare Delivery System01:29

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.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Multimodal generative AI for medical image interpretation.

Vishwanatha M Rao1,2, Michael Hla1,3, Michael Moor4,5

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Nature
|March 27, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers multimodal generative medical image interpretation (GenMI) to automate report generation from medical images. While promising for clinical support, challenges in accuracy and transparency must be addressed for reliable implementation.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Clinical Report Generation

Background:

  • Interpreting medical images and generating reports is crucial but burdensome for clinicians.
  • Multimodal generative medical image interpretation (GenMI) using AI presents new automation opportunities.

Purpose of the Study:

  • To synthesize progress and challenges in AI for generating medical reports from images.
  • To advocate for a novel paradigm for deploying GenMI to empower clinicians and patients.

Main Methods:

  • Analysis of current AI models for medical report generation, focusing on radiology.
  • Review of strengths, applications, and challenges of GenMI systems.

Main Results:

  • GenMI shows potential to match human expert performance in report generation across disciplines like radiology, pathology, and dermatology.
  • Significant obstacles remain in validating model accuracy, ensuring transparency, and capturing nuanced clinical impressions.

Conclusions:

  • Careful implementation of GenMI can assist clinicians, improve care quality, enhance education, reduce workloads, and expand access to expertise.
  • Developing multimodal generative AI requires addressing key challenges to complement human experts in reliable medical report writing.