Monday March 25, 2019
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Novel AI tool may help tackle substance abuse in youth

The team tackled this problem from an AI perspective, creating an algorithm that takes into account both how the individuals in a subgroup are connected and their prior history of substance abuse

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Societal pressure and lack of support often forces young adults to indulge in impulsive self-harm mechanisms like drug overdose and self cutting. Pixabay
  • New AI tool may help tackle substance abuse in youth
  • The AI uses a new type of algorithm
  • Group therapy is one way of curing the abuse

Researchers have developed a novel algorithm-based on artificial intelligence (AI) that leverages social networks to optimise substance abuse intervention groups for the homeless youth.

The algorithm categorises participants, who voluntarily work on recovery, into smaller groups, or subgroups, in a way that maintains helpful social connections and breaks social connections that could be detrimental to recovery and inadvertently expose them to negative behaviours.

Drugs, Rehabilitation centre
This new Ai can help teens suffering from substance abuse. Pixabay

“We know that substance abuse is highly affected by social influence; in other words, who you are friends with,” said Aida Rahmattalabi, a post-doctoral student at the University of Southern California in the US.

“In order to improve the effectiveness of interventions, you need to know how people will influence each other in a group,” Rahmattalabi added. While group therapy can offer support to homeless youth, if not structured properly, they can also lead to friendships based on anti-social behaviour.

Also Read: Women-Centric Drug Rehabilitation Centers in Hyderabad is Saving Young Girls from Recreational Drugs like LSD

The team tackled this problem from an AI perspective, creating an algorithm that takes into account both how the individuals in a subgroup are connected — their social ties — and their prior history of substance abuse.

Drug overdose
Group theray can save many lives. Pixabay

Survey data gathered voluntarily from homeless youth, as well as behavioural theories and observations of previous interventions, were used to build a computational model of the interventions.

“Based on this we have an influence model that explains how likely it is for an individual to adopt negative behaviours or change negative behaviours based on their participation in the group,” Rahmattalabi noted. “This helps us predict what happens when we group people into smaller groups,” she said. IANS

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Google Claims Eye Doctors Can Turn More Effective Using AI

Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm. 

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The research team at Google AI believes that some of these pitfalls may be avoided if the computer can "explain" its predictions. Pixabay

As Artificial Intelligence (AI) continues to evolve, diagnosing diseases has become faster with greater accuracy. A new study from the Google AI research group shows that physicians and algorithms working together are more effective than either one alone.

In the study, to be published in the journal Ophthalmology, the researchers created a system which not only improved the ophthalmologists’ diagnostic accuracy but also improved the algorithm’s accuracy.

The study expands on previous work from Google AI showing that its algorithm works roughly as well as human experts in screening patients for a common diabetic eye disease called diabetic retinopathy.

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To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance. Pixabay

“What we found is that AI can do more than simply automate eye screening, it can assist physicians in more accurately diagnosing diabetic retinopathy. AI and physicians working together can be more accurate than either one alone,” said lead researcher Rory Sayres.

Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said.

According to the team, previous attempts to use computer-assisted diagnosis shows that some screeners rely on the machine too much, which leads to repeating the machine’s errors, or under-rely on it and ignore accurate predictions.

The research team at Google AI believes that some of these pitfalls may be avoided if the computer can “explain” its predictions.

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Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said. Pixabay

To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance.

Also Read: U.S. Government Human Rights Report Shows ‘Amber’ Warning Light Situation in Hong Kong

Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm.

With assistance, general ophthalmologists match but do not exceed the model’s accuracy, while retina specialists start to exceed the model’s performance. (IANS)