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  • AI in Healthcare: Who Pays When Things Go Wrong? 🤔

AI in Healthcare: Who Pays When Things Go Wrong? 🤔

AND: Google AI Diagnoses Disease from a Cough, AI Helps Idenfity Tiny Cancers Missed by Radiologists, Google Street View Predicts Heart Disease Risk

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Dear subscribers,

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A heartfelt thank you for joining us on this journey. We are just getting started as we explore this fascinating and fast-evolving world of AI in Healthcare.

Let’s get into it!



  • Google’s AI Could Diagnose Disease from a Simple Cough

  • AI in Healthcare: Who Pays When Things Go Wrong?

  • AI Helps Identify Tiny Cancers Missed by Radiologists

  • Your Neighborhood Can Help Predict Your Heart Health

  • Generative AI Use Case in Healthcare (INFOGRAPHIC)

Read time: under 5 minutes


Google's AI Could Diagnose Diseases from a Simple Cough

Inspired by healthcare workers' observations that they could sometimes identify COVID-19 patients by their cough, Google researchers embarked on a project to see if AI could diagnose diseases based on sound recordings. This resulted in the release of a new AI system named Health Acoustic Representations (HeAR).

How it Works:

HeAR is trained on a massive dataset of over 300 million audio clips from YouTube videos, including coughs, breathing, and other human sounds.

Unlike similar models, HeAR uses self-supervised learning, which allows it to learn from data that hasn't been categorized (unlabeled), overcoming limitations caused by the scarcity of labeled medical datasets.

Acoustic science has existed for decades. What’s different is that now, with AI and machine learning, we have the means to collect and analyse a lot of data at the same time.

Yael Bensoussan, co-founder of the Bridge2Ai Voice Consortium

Promising Results:

In early tests, HeAR performed well in detecting COVID-19 and tuberculosis, exceeding the performance of existing models.

While still under development (no FDA approval yet), HeAR carries potential to transform disease diagnosis and treatment in a non-invasive and cost effective way.


AI in Healthcare: Who Pays When Things Go Wrong?

The rise of AI in healthcare raises a crucial question: who is responsible if the technology leads to a misdiagnosis or treatment error?

It's a complicated question, and there's no easy answer.

  • Doctors are worried about lawsuits. They're concerned that they'll be held responsible for mistakes made by AI tools, even if they did everything right.

  • Lawmakers are considering "safe harbor" protections for doctors who use AI in patient outcome monitoring programs. This would provide some legal protection for doctors, but it's not a perfect solution.

  • There are also concerns about the liability of AI developers. If an AI tool malfunctions and causes harm, who is responsible? The developer? The doctor who used the tool? The patient?

Here are a few things that can be done to address this issue:

  • Develop clear regulations for the use of AI in healthcare. These regulations should be designed to protect patients and ensure that AI tools are safe and effective.

  • Invest in research on AI safety. We need to better understand the risks of AI and how to mitigate them.

  • Educate doctors and patients about AI. Doctors need to be aware of the potential risks and benefits of AI, and patients need to be able to make informed decisions about whether or not to use AI tools.


AI Helps Identify Tiny Cancers Missed by Radiologists

Barbara, one of the trial patients, had her tumor caught early thanks to Mia AI

An AI tool named Mia, tested by the NHS in the UK, has made a significant breakthrough in early breast cancer detection, uncovering tiny tumors that were overlooked by human doctors.

Here are the key takeaways from this advancement:

Early Detection Success: Mia identified breast cancer signs in 11 women missed by doctors during a pilot with over 10,000 participants.

Invisible to the Eye: The AI spotted very small tumors, practically invisible to human radiologists, showcasing its precision and potential to save lives.

Quick and Efficient: Mia not only flags cancer more accurately but could also reduce the waiting time for results from 14 to 3 days, enhancing patient experience and reducing anxiety.

Workload Reduction: The technology aims to halve radiologists' workload by replacing one of the two human reviews typically required for each scan, without compromising accuracy.


Google Street View Predicts Heart Disease Risk

Imagine if your neighborhoods and the streets you are walking in could predict your heart health. A groundbreaking study in the European Heart Journal has unveiled that possibility.

Researchers used AI and Google Street View images of over half a million locations across 7 US cities to study how neighborhoods influence heart disease risk. They found a striking 63% correlation between features like green spaces and walkable streets, and the prevalence of coronary artery disease. This suggests that the way our cities are built can significantly impact heart health.

By using AI to analyze neighborhoods at scale, researchers hope to guide future urban planning towards creating healthier communities that promote physical activity and reduce heart disease risk.


Generative AI Use Cases in Healthcare

What is Generative AI?

Generative AI is a subset of artificial intelligence that learns from large volumes of data to create new content, such as text and images. According to Accenture, more than 50% of healthcare organisations plan to run ChatGPT-based pilots this year alone.

Learn more about the use cases of generative AI in healthcare in the infographic below:

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