Deep Learning in Medical Diagnosis: How AI Saves Lives and Cuts Treatment Costs

AI in disease detection: the current state of things

  • the continued growth of computing power and storage technologies,
  • declining cost of hardware,
  • rising cost of healthcare,
  • the shortage of healthcare workers, and
  • an abundance of medical data to train models. In the US alone, 60 billion radiology images are generated annually — not to mention other data.

Breast cancer screening

AI advancement and promised benefits

Commercially available solutions

Early melanoma detection

AI advancement and promised benefits

That’s how a CNN developed at Stanford classifies skin lesions from images. Source: ExtremeTech

Commercially available solutions

Lung cancer screening

AI advancement and promised benefits

Commercially available solutions

Veye Chest analyzes nodules using AI.

Diabetic retinopathy screening

AI advancement and promised benefits

Five levels of DR severity detected on retinal images. Source: Adafruit

Commercially available solutions

Cardiac risk assessment from electrocardiograms (ECGs)

AI advancement and promised benefits

Commercially available solution

Early stroke diagnosis from head CT scans

AI advancement and promised benefits

In most cases, AI algorithms sufficiently differentiate ischemic strokes caused by blood clots from hemorrhagic strokes caused by bleeding. Source: Young Scientist Journal

Commercially available solutions

Barriers to ML adoption in healthcare

Regulatory issues

Shortage of data on new diseases

AI-assisted diagnosis for COVID-19 from computed tomography scans. Built in China, the intelligent system still lacks data to be broadly adopted. Source: medRxiv

Data silos and privacy rules

Lack of standardization

Black box aspect and lack of trust

The difference between today’s ML models and XAI. Source: DARPA

AI vs MD: who’s the boss here?

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