Google introduces AI tool to self diagnose skin conditions!

|Artificial Intelligence| |Machine Learning| |Deep Learning|

-Sreemeenakshi V

Google’s latest entry into health care is a web tool that uses artificial intelligence to help people identify skin, hair, or nail conditions. The company previewed the tool, and they hope to launch a pilot later this year.

Google shared a preview of this AI-powered dermatology assist tool that helps you in understanding what is going on with issues associated with your body’s skin, hair and nails. Using many of the same techniques that detect diabetic eye disease or lung cancer in CT scans, this tool can get you closer to identifying dermatologic issues like a rash on your arm that’s been bugging you, using your phone’s camera.

Once users launch the tool, they will use their phone’s camera to capture three pictures of their hair, skin, or nail concern from different angles. The app then asks questions about the person’s skin type, how long they’ve had the issue, and other symptoms that will help the tool determine the possible cause. The AI algorithm analyzes this information and draws from its knowledge of 288 conditions to give users a list of possible conditions that can then be researched further.

To make sure they’re building for everyone, the model accounts for factors like age, sex, race and skin types — from pale skin that does not tan to brown skin that rarely burns. The Google digital intelligence tool extracts about 65,000 skin images and case data to get more reliable output.

It is important to note here that it is very easy to use since all you would need is a web-based application, three photos of the affected area of ​​the skin and wait for the preliminary diagnosis.

For each matching condition, the tool will show you the dermatologist reviewed information and answers to commonly asked questions. It also comes along with similar matching images from the web.

The tool is the product of over three years of machine learning research and merchandise. A 2020 study published in ‘Nature Medicine’ showed how the tech’s deep learning approach achieved a level of accuracy similar to that of board-certified dermatologists.

Adding thereto, recent research from Google demonstrated how non-specialist doctors can use AI-based tools to enhance their ability to interpret skin conditions.

Recently, the AI model that powers the tool has successfully passed the clinical validation, and it has been CE marked as a Class I medical device in the EU. In the coming months, they’ve plan to build on this work so more people can use this tool to answer questions about common skin issues.

What is Artificial Intelligence?

Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required the need for human intelligence.

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines that are capable of performing tasks that typically require human intelligence. Artificial Intelligence was born to make life much easier for us, and within it is modern medical care.

Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess), self-driving cars and many others. AI is being used widely across different industries including finance and healthcare.

The main purpose of Artificial Intelligence itself is to aid human capabilities and help us make advanced decisions with far-reaching consequences.

The goals of artificial intelligence include:

• Learning
• Reasoning
• Perception

Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are more complex.

As technology advances, previous benchmarks that defined artificial intelligence has become outdated. AI is continuously being evolved to benefit many different industries.

Artificial intelligence can be divided into two different categories:

• Weak Artificial intelligence

Weak AI systems include video games and personal assistants such as Amazon’s Alexa and Apple’s Siri. When you ask the assistant a question, it answers it for you. These focuses on performing a specific task and cannot perform multiple types of task.

• Strong artificial intelligence

Strong AI systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and even more complicated systems. They are programmed to handle situations in which they may require to problem solve without having a person intervene. These kinds of systems are likely to be found in applications like self-driving cars or in hospital operating rooms. Strong AI can perform a variety of functions, eventually teaching itself to solve for new problems.

3 Types of Artificial Intelligence

• Artificial Narrow Intelligence (ANI)

This is the most common form of AI that you’d find in the market now. These Artificial Intelligence systems are designed to solve a single problem and would be able to execute even a single task really well. As its name states, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They’re able to come close to human functioning in very specific contexts, and may even surpass them in many instances.

• Artificial General Intelligence (AGI)

AGI is still a theoretical concept. It’s defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

• Artificial Super Intelligence (ASI)

ASI is likely to be seen as the logical progression from AGI. An Artificial Super Intelligence (ASI) system might someday be able to surpass all human capabilities. This would include decision making or taking rational decisions.
Once Artificial General Intelligence is achieved, AI systems would rapidly be able to improve their capabilities and advance into realms that we might not even have dreamed of, while the gap between AGI and ASI would be relatively narrow.

2 Main subsets of AI are:

• Machine Learning
• Deep Learning

Machine Learning:

Machine learning enables a computer to make predictions or even decisions using historical data without being explicitly programmed. It is the study of computer algorithms that improves automatically through experiences and by the use of data. It’s seen as a part of artificial intelligence.

Machine learning algorithms can be used in a wide range of applications, such as in fields of medicine, email filtering, and computer vision, where it could be difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Machine learning involves computers to discover how they would be able to perform tasks without actually being explicitly programmed to do so. It involves computers learning from the data provided so that they could carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps that are required to solve the problem at hand and on the computer’s part, no learning is needed. In cases that involves more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it could turn out even more effective and efficient to help the machine develop its own algorithm, rather than having human programmers specify every needed step.

Here we feed in data or the Input + Output, run it on machine during training and the machine creates its own program(logic), which can be evaluated while testing. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Deep Learning:

Deep learning which is also known as deep structured learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

Deep learning is one of the key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers, etc. Lately, deep learning has been getting lots of attention. In deep learning, a computer model learns to perform classification tasks directly from text, images or sound. Deep learning models can achieve state of the art accuracy, which could sometimes exceeding human-level performance.

Deep learning achieved recognition and accuracy at higher levels than ever before. This helps consumer electronics to meet the users expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning has outperformed humans in some tasks like classifying objects in images.

Its application extends to fields of medicines as well where, Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA has built an advanced microscope that could yield a higher dimensional data set that is used to train a deep learning application to accurately identify cancer cells.

Deep learning models are being trained by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.

One of the most popular types of deep neural networks is known as the Convolutional Neural Networks, CNN. A CNN coils together the learned features with input data, and uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images.

Artificial Intelligence vs Machine Learning

Although Artificial intelligence and Machine learning are two related technologies, they cannot be used as a synonym for each other. They are two different terms in various cases.

AI is a bigger concept to create intelligent machines that can simulate human thinking capacity and behavior, whereas, Machine Learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.

AI implies an agent interacting with the environment to learn and take actions that maximize its chance to successfully achieve its goals, whereas Machine Learning learns and predicts based on passive observations.

The aim of AI is to increase the chance of success and not accuracy, whereas Machine Learning aims at increasing the accuracy but it does not care about success.

AI leads to develop a system that mimics human in responding and behaving to a given circumstance whereas Machine Learning involves in creating self learning algorithms.

AI is a computer algorithm which exhibits intelligence through decision making whereas Machine Learning is an AI algorithm which allows system to learn from data. And Deep Learning is a ML algorithm that uses deep(more than one layer) neural networks to analyze data and provide output accordingly.

Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is a term used for any computer program that does something smart. It can be concluded that, all machine learning is AI, but not all AI is machine learning.

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