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Commentators and Journalists Weigh In On Digital Health And Related Privacy, Safety, Social Media And Security Matters. Lots Of Interesting Perspectives - October 11, 2022.
-----This weekly blog is to explore the news around the larger issues around Digital Health, data security, data privacy, AI / ML. technology, social media and any related matters.I will also try to highlightADHA Propagandawhen I come upon it.Just so we keep count, the latest Notes from the ADHA Board were dated 6 December, 2018 and we have seen none since! It ’s pretty sad!Note: Appearance here is not to suggest I see any credibility or value in what follows. I will leave it to the reader to decide what is worthwhile and what is not! The point is to let people know what is being said / published that I have come upon, a...
Source: Australian Health Information Technology - October 11, 2022 Category: Information Technology Authors: Dr David G More MB PhD Source Type: blogs

Weekly Overseas Health IT Links –27th August 2022.
In this study, researchers gathered a diverse group of participants; 43 percent were Black, and 68 percent were women. They also considered factors such as age and insurance status when drawing conclusions.The study occurred through a clinical trial, where all participants were randomly assigned to have their next visit occur through either phone or video-based platforms. The central unit of measurement was visit satisfaction rate, reported on a ten-point scale. Researchers noted noninferiority data based on whether patient satisfaction between the telehealth methods exceeded a -15 percent margin.-----https://www.theverge....
Source: Australian Health Information Technology - August 27, 2022 Category: Information Technology Authors: Dr David G More MB PhD Source Type: blogs

Explain yourself, machine. Producing simple text descriptions for AI interpretability
We describe a feature, give a location, and then synthesise a conclusion. For example: There is an irregular mass with microcalcification in the upper outer quadrant of the breast. Findings are consistent with malignancy. You don’t need to understand the words I used here, but the point is that the features (irregular mass, microcalcification) are consistent with the diagnosis (breast cancer, malignancy). A doctor reading this report already sees internal consistency, and that reassures them that the report isn’t wrong. An common example of a wrong report could be: Irregular mass or microcalcification. No ...
Source: The Health Care Blog - December 12, 2019 Category: Consumer Health News Authors: Christina Liu Tags: Artificial Intelligence Health Tech AI Luke Oakden-Rayner machine learning Radiology Source Type: blogs