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Virtual Care is Healthcare, ABAIM 👨‍⚕️

Virtual care is healthcare. It is a paradigm shift in healthcare delivery. It’s not just a video conferencing tool, but it will change the way we deliver care for the next century.

Telehealth is the gateway drug to digital health!

Paradigm Shift

  1. Steps to Digitization
  2. Pandemic & Contactless Care
  3. Virtual Hospitals
  4. Big Tech in Virtual Care
  5. Cultural Shift to Remote work, learn & healthcare
  6. Impact on Clinicians+

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AI in Healthcare

ABAIM Course Review & Testimonial

| Website: | Course: |

Read my full review below

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Interpretability vs explainability: It’s mostly about trust

.. The two terms explainability and interpretability are used interchangeably to explain transparency and trust but it is worth discussing the difference …

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Why AI is Harder Than We Think

… In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense …

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Everyone wants to do the model work, not the data work

AI models are increasingly applied in high-stakes domains like health and conservation. Data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact, impacting predictions like cancer detection, wildlife poaching, and loan allocations …

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Attention in the Human Brain and Its Applications in ML

The importance weighing process of attention is intuitive from a machine learning perspective. Not all parts of the input (or encoded input, extracted features, embedding, etc.) have the same importance in generating (decoding) expected output.

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Remote Ischemic Conditioning With Exercise (RICE)

This study is a prospective randomized controlled trial to determine the rehabilitative effect of early RIC followed by exercise on patients with acute ischemic stroke

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Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network

We developed a deep learning neural network that performs personalized detection of RNS-derived ictal patterns with expert-level accuracy. These results suggest the potential for automated techniques to significantly improve the management of closed-loop brain stimulation, including during the initial period of recording when the device is otherwise naïve to a given patient’s seizures

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Learn: 1) Digital Health 2) AI in Healthcare 3) Value-Based Care 4) Neurology

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Junaid Kalia

Junaid Kalia

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