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December 14, 2021

Artificial Intelligence in the Healthcare Sector | Examples and Use Cases

The past few years have shown us two conflicting realities: how valuable and limited our healthcare resources are and the importance of treating everyone who needs care when they need it. New Artificial Intelligence (AI) approaches are positioning themselves as critical tool to alleviate these limitations.

These assistant AIs assist expert staff by increasing their patient care capacity. Applying these techniques has already brought significant improvements in business processes in sectors such as energy or industry. Moreover, in the healthcare sector, it has the opportunity to have a very positive impact on the most human side of our society.

An example of these approaches is robotic process automation (RPA) applications, driven by Artificial Intelligence to detect symptoms quickly and anticipate harsher repercussions.

In the field of preventive medicine, we are already facing cases in which technology is achieving significant advances in preventive medicine, such as the early detection of melanoma using Computer Vision techniques; the early detection of breast cancer using non-contact, non-radiation temperature sensors; obtaining results of blood biometry in just 10 minutes; or progress in imaging diagnoses based on Deep Learning.

What seemed like a utopia until a few years ago is now a reality, and Artificial Intelligence is revolutionizing the health sector in the detection of diseases. We tell you everything!

Artificial Intelligence in the Early Detection of Cancer

Cancer is the leading cause of death globally and a major barrier to increasing life expectancy. WHO estimates that between 2000 and 2019, cancer was the first or second leading cause of death before age 70 in 112 out of 183 countries. In addition, if we look at Asia, we find that 58.3% of the cases in 2020 occurred in this continent, and the forecasts are not much better, especially for India.

New technologies are a great ally in health care when it comes to stopping cancer. This is where integrating AI models to improve diagnostic accuracy and speed, assist in data-based clinical decision-making and achieve better outcomes comes into play.

The projects ‘Inner Eye’ and ‘Bio Model’

Cancer is a complex disorder with thousands of genetic and epigenetic variations. Advances in AI-based algorithms allow these genetic mutations and interactions of dangerous proteins to be identified at an early stage. Microsoft, for example, has created ‘Inner Eye’, a solution that uses Machine Learning (ML) and Natural Language Processing (NLP) to help oncologists define the most effective treatment for each of their patients. It reaches such a level of detail, that it predicts how individual cells will respond within the tumor according to which treatment.

Also from Microsoft, we found Bio Model Analyzer (BMA), a cloud tool that allows biologists to model how cells interact and communicate with each other. This, in addition, has multiple applications apart from its screening function, such as understanding the best way to treat cancer by designing which drugs will be most effective and at what point the disease could become resistant to them.

Here are just a few examples of how AI can help cancer prevention and treatment, but more studies and research are emerging.

Artificial Intelligence in the Detection of Rare Diseases

In the field of rare diseases, being less common, diagnosing them is much more difficult. There are more than 6,000 known rare diseases, and the use of Artificial Intelligence has been a significant advance in making an accurate and timely diagnosis.

Machine Learning to Give a Correct Diagnosis

If we look at the figures, 40% of children with rare congenital diseases receive a first misdiagnosis, and this situation can be repeated several times, even years. Accelerating and improving this method can potentially help more than 446 million people worldwide, and AI can help meet this challenge.

The different models used in the platform, together with genomic analysis, collect the data and analyze the similarities between the parameters of disease clusters and patients. With the correct data, the AI can determine whether a patient’s symptoms are for common or rare diseases. In addition, thanks to classification techniques such as decision trees, neural networks, and random forests, it is possible to find out the correct group to which a patient belongs and divide them into subgroups that have similar diagnoses.

Another example is the use of Learning Transfer, which can be applied in diagnostic care to aggregate data from electronic medical records and develop a patient model to determine the information needed for an accurate diagnosis.

From Plain Concepts, we develop, for Foundation29, an AI-assisted platform built to facilitate the analysis and diagnosis of rare diseases. The model processes reports from different sources to extract symptoms and encode them. He then launches an ML algorithm to classify thousands of mutations in the patient, and AI suggests new symptoms for the doctor to contrast. We tell you all the details of the project in the talk given by Pablo Botas in the edition of Singularity Tech 2019:

Dx29: Support for the diagnosis of rare genetic diseases with AI.

Artificial Intelligence to Detect Alzheimer’s

We also find applications of AI in the field of cognitive impairment diseases. Technological solutions are being developed that scan speech and vocabulary patterns to detect early signs of Alzheimer’s, the most common cause of dementia. It is a complex disease to treat and is easily confused with conventional mild cognitive impairment associated with aging. affordable and accessible way than traditional medical tests

Thanks to AI, subtle changes in speech and behavior can be detected more quickly and reliably than human observations. The goal is a smartphone app that facilitates diagnosis in a more affordable and accessible way than traditional medical tests. Thanks to NLP, the algorithm can pore over the text of patient conversations, examining the variety of words people use to assess their cognitive state and record time. This is just a first step, as the next step would be to use Computer Vision to analyze facial expressions and the words patients say in an interview and examine the acoustic cues of the conversation. The application would assimilate all this data and then score users on their risk and likelihood of showing signs of dementia.

The ultimate goal of all these assistive tools is to make the diagnostic process easier for the physician. Making these tools available to the public could also help millions of people to detect their ailments early and seek medical help, speeding up their access to appropriate treatment and delaying the effects of the disease.

These are just a few examples of what Artificial Intelligence can do to help in the healthcare sector, but there are many more. From Plain Concepts, we grow thanks to projects that represent a challenge to innovating and reinventing any industry thanks to technology. The healthcare sector has become an environment where we love to research and revolutionize traditional tools with never-before-seen developments (whether with AI, Augmented Reality, Big Data…). And the result can be seen in our Healthcare Case Studies.

Elena Canorea
Elena Canorea
Communications Lead