5 Ways Machine learning is Redefining Healthcare

5 Ways Machine learning is Redefining Healthcare

Machine learning (ML) is an application of artificial intelligence (AI) wherein the system looks at observations or data, such as examples, direct experience, or instruction, figures out patterns in data and predicts events in the future based on...

Machine learning (ML) is an application of artificial intelligence (AI) wherein the system looks at observations or data, such as examples, direct experience, or instruction, figures out patterns in data and predicts events in the future based on the examples that we provide. Machine learning is seeing more and more use across industries for various reasons: vast amounts of data are being captured and made available digitally; processing of large amounts of data has become cost-effective due to the increased computing power now available at affordable prices; and various open source frameworks, toolkits and libraries are available that can be used to build and execute ML applications.

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Specifically in healthcare, ML has led to exciting new developments that could redefine cancer diagnosis and treatment in the years to come. ML can increase access to treatment in developing countries which don’t have enough specialist doctors that can treat certain diseases, it can improve the sensitivity of detection, add more value in treatment decisions, and it can help personalize treatment so that each patient gets the treatment that’s best for them. In many cases they can even add to workflow efficiency in hospitals. The possibilities are endless.

Identifying Disease And Diagnosis

With growing populations and increased life expectancy, health systems are quickly becoming overburdened, under-resourced and not equipped for the challenges they face. Scientists have been working on ML models that predict disease susceptibility or aid in early diagnosis of diseases and illnesses. UK-based technology start-up Feebris is using artificial intelligence algorithms for the precise detection of complex respiratory conditions in the field. It connects to existing medical sensors and can be used by non-doctor users to identify respiratory issues early, avoiding complications and hospitalizations. In what could be an absolute game-changer, MIT’s Computer Science and Artificial Intelligence Lab has developed a new deep learning-based prediction model that can forecast the development of breast cancer up to five years in advance. Their model was trained on mammograms and patient follow-up data to identify patterns that would not be obvious to or even observable by human clinicians. The results have so far shown to be far more precise, especially at predictive, pre-diagnosis discovery.

Medical Imaging Diagnosis

IBM researchers estimate that medical images are the largest data source in the healthcare industry. ML algorithms can process massive amounts of medical images at rapid speeds. And they can be trained to be extremely precise in identifying miniscule details in CT scans and MRIs. Companies such as Enlitic, Zebra Medical Vision and Sophia Genetics have developed ML algorithm-based analysis of all types of medical imaging reports and can diagnose malignancies or abnormalities with a higher accuracy rate than healthcare professionals. LYNA (LYmph Node Assistant) by Google detects spread of breast cancer metastasis early and can reduce the burden on pathologists as well. A deep learning convolutional neural network or CNN—developed by a team from Germany, France and the US—can diagnose skin cancer more accurately than dermatologists. In a recently reported study, the software was able to accurately detect cancer in 95% of images of cancerous moles and benign spots, whereas a team of 58 dermatologists was accurate 87% of the time.

The move from lab to actual practice has happened already for some AI-based solutions such as the FDA-approved imaging tool called IDx-DR for diagnosing diabetic eye disease.

Robotic Surgery

Robotics is changing the way surgery is performed today. The da Vinci robot is designed to facilitate complex surgery using a minimally invasive approach, reducing the length of surgeries and subsequently hospital stays. Various other robotic tools such as Stereotaxis in cardiac catheterization, Medtronic/Mazor in spine and neurology, Accuray in cancerous tumor irradiation, Stryker’s Mako in orthopedic hip and knee replacement are improving surgical outcomes for thousands of patients. Even dental implants and hair transplants are being performed by surgical robots today.

AI and ML-based techniques will enhance the precision of surgical tools by incorporating real-time data, feedback from previous successful surgeries and data from electronic medical records during the surgery itself. This can help reduce human error and help general surgeons to perform complex surgeries in resource-limited settings lacking specialists.

Personalized Medicine

By applying AI and ML to multiple data sources—genetic data, electronic health records, sensor/wearables data, environmental and lifestyle data—researchers are taking first steps toward developing personalized treatments for diseases from cancer to depression. IBM Watson Oncology is making great strides in cancer treatment by leveraging patient medical history to help generate multiple treatment options. Similarly, a test named ‘CanAssist Breast’ uses ML to identify a novel combination of biomarkers which play key role in recurrence of breast cancer. The test predicts the risk of recurrence for every patient. This helps personalize treatment by allowing patients with a low risk of cancer recurrence to receive less aggressive treatment.

Drug Development

ML can be applied at all stages of new drug discovery including designing the chemical/protein structure of drugs, target validation, investigating drug safety and managing clinical trials. The hope is that use of ML in drug discovery will not only help significantly reduce the cost of introducing new drugs to the market, but also make the drug discovery process faster (currently 10-15 years including clinical trials) and more cost-effective (currently costs almost $1 billion per new drug). AI company Atomwise’s platform AtomNet uses deep learning software to sift through millions of possible molecules in a day or two, which would normally take months via traditional methods. The software then analyzes simulations that show how the potential medicine will behave in the human body. It has been able to identify possible medicines for multiple sclerosis and the deadly Ebola virus. Deepmind, the AI arm of Google’s parent Alphabet Inc, is also making huge progress in this field.

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Thus, it can be seen that AI indeed has tremendous potential and all stakeholders like the promising algorithms, accurate clinical and relevant in vivo data, clinicians, institutions have to align themselves to reap meaningful benefits from it.

One must remember that excellent technical innovations in AI can not fix social/political problems. Also the data input to AI must be in high volume and of clinically high quality/relevance. Fundamentally flawed data cannot substitute for high volume. Currently most of the AI applications are using the paradigm of ‘deductive reasoning’ and we need to move from towards ‘inductive reasoning’.

We have travelled fair amount in the AI path to excellence but one must be cautious going further to embrace the brilliant promise it holds. What we need next is to move from theoretic benefit and evangelical sales to established use cases and robust, clinically-relevant data.

An Introduction to Artificial Intelligence (AI)

An Introduction to Artificial Intelligence (AI)

In this Introduction to Artificial Intelligence and in computer science, artificial intelligence (AI), sometimes called machine intelligence. Before leading to the meaning of artificial intelligence let understand what is the meaning of the Intelligence - Intelligence. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"

Before leading to the meaning of artificial intelligence let understand what is the meaning of the Intelligence - Intelligence: The ability to learn and solve problems. This definition is taken from webster’s Dictionary.

The most common answer that one expects is “to make computers intelligent so that they can act intelligently!”, but the question is how much intelligent? How can one judge the intelligence?

…as intelligent as humans. If the computers can, somehow, solve real-world problems, by improving on their own from the past experiences, they would be called “intelligent”.
Thus, the AI systems are more generic(rather than specific), have the ability to “think” and are more flexible.

Intelligence, as we know, is the ability to acquire and apply the knowledge. Knowledge is the information acquired through experience. Experience is the knowledge gained through exposure(training). Summing the terms up, we get artificial intelligence as the “copy of something natural(i.e., human beings) ‘WHO’ is capable of acquiring and applying the information it has gained through exposure.”

Intelligence is composed of:
  • Reasoning
  • Learning
  • Problem Solving
  • Perception
  • Linguistic Intelligence

Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuro-science, artificial psychology and many others.

Need for Artificial Intelligence
  1. To create expert systems which exhibit intelligent behavior with the capability to learn, demonstrate, explain and advice its users.
  2. Helping machines find solutions to complex problems like humans do and applying them as algorithms in a computer-friendly manner.

Applications of AI include Natural Language Processing, Gaming, Speech Recognition, Vision Systems, Healthcare, Automotive etc.

An AI system is composed of an agent and its environment. An agent(e.g., human or robot) is anything that can perceive its environment through sensors and acts upon that environment through effectors. Intelligent agents must be able to set goals and achieve them. In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that cannot only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment. Natural language processing gives machines the ability to read and understand human language. Some straightforward applications of natural language processing include information retrieval, text mining, question answering and machine translation. Machine perception is the ability to use input from sensors (such as cameras, microphones, sensors etc.) to deduce aspects of the world. e.g., Computer Vision. Concepts such as game theory, decision theory, necessitate that an agent be able to detect and model human emotions.

Many times, students get confused between Machine Learning and Artificial Intelligence, but Machine learning, a fundamental concept of AI research since the field’s inception, is the study of computer algorithms that improve automatically through experience. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as a computational learning theory.

Stuart Shapiro divides AI research into three approaches, which he calls computational psychology, computational philosophy, and computer science. Computational psychology is used to make computer programs that mimic human behavior. Computational philosophy is used to develop an adaptive, free-flowing computer mind. Implementing computer science serves the goal of creating computers that can perform tasks that only people could previously accomplish.

AI has developed a large number of tools to solve the most difficult problems in computer science, like:
  • Search and optimization
  • Logic
  • Probabilistic methods for uncertain reasoning
  • Classifiers and statistical learning methods
  • Neural networks
  • Control theory
  • Languages

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, prediction of judicial decisions[204] and targeting online advertisements. Other applications include Healthcare, Automotive
Finance, Video games etc

Are there limits to how intelligent machines – or human-machine hybrids – can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. ‘’Superintelligence’’ may also refer to the form or degree of intelligence possessed by such an agent.

**References: **https://en.wikipedia.org/wiki/Artificial_intelligence

Natural Language Processing

Natural Language Processing, or NLP for short, is comprehensively characterized as the programmed control of regular language, similar to discourse and content, by programming. The investigation of normal language handling has been around for...

Natural Language Processing, or NLP for short, is comprehensively characterized as the programmed control of regular language, similar to discourse and content, by programming.
The investigation of normal language handling has been around for over 50 years and became out of the field of phonetics with the ascent of PCs.
Check out this NLP tutorial for more insights.

Natural Language Processing (NLP) is tied in with utilizing apparatuses, systems and calculations to process and comprehend characteristic language-based information, which is generally unstructured like content, discourse, etc. In this arrangement of articles, we will be taking a gander at attempted and tried systems, strategies and work processes which can be utilized by professionals and information researchers to remove valuable bits of knowledge from content information. We will likewise cover some helpful and intriguing use-cases for NLP. This article will be tied in with handling and understanding content information with instructional exercises and hands-on models. People choose NLP training to gain expertise and become NLP engineer.

Artificial Intelligence (AI) Tutorial - Getting started with AI

Artificial Intelligence (AI) Tutorial - Getting started with AI

Artificial Intelligence (AI) Tutorial - Getting started with Artificial Intelligence. In this Artificial Intelligence tutorial you will learn end to end about AI and it's vast domain. So this AI tutorial for beginners is an exhaustive tutorial for you to get started with AI.

In this Artificial Intelligence (AI) tutorial you will learn end to end about AI and it's vast domain. So this AI tutorial for beginners is an exhaustive tutorial for you to get started with AI.