The past few years have shown us two conflicting realities: how precious 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 tools to mitigate these limitations.

These assistants help expert staff by increasing their ability to care for patients. The application of these methods has already led to significant improvements in business processes in sectors such as energy or industry. Moreover, AI in the healthcare sector has the potential to have a very positive impact on the human side of our society.

An example of these approaches is process automation applications (RPA) driven by artificial intelligence to detect symptoms—which can quickly anticipate more severe consequences.

In the field of preventive medicine, we are already seeing cases where technology is making significant advances in preventive medicine, such as:

• Early detection of melanoma using Computer Vision methods
• Early detection of breast cancer using non-contact temperature sensors without radiation
• Quick results of blood biometrics in just 10 minutes
• Progress in diagnostics based on deep learning

What only seemed like a utopia a few years ago has become reality, and artificial intelligence is revolutionizing the healthcare sector in disease detection.

Also Read: 7 Steps of Create a Successful Healthcare Mobile Application

Artificial Intelligence in Early Cancer Detection

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

New technologies are a great ally in healthcare when it comes to ending cancer. This is where the integration of AI models improves the accuracy and speed of diagnosis, aids in data-driven clinical decision-making, and achieves better results.

Projects “Inner Eye” and “Bio Model”

Cancer is a complex disorder with thousands of genetic and epigenetic variations. Advances in AI-based algorithms are making it possible to identify these genetic mutations and dangerous protein interactions 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 determine the most effective treatment for each of their patients. It reaches such a level of detail that it predicts how individual cells will respond in a tumor, according to which treatment is necessary.

Also from Microsoft is the Bio Model Analyzer (BMA), a cloud-based tool that allows biologists to model how cells interact with each other. It also has several applications beyond the screening function, such as understanding the best way to treat cancer by developing which drugs will be most effective and at what point diseases may become resistant to them.

And these are just a few examples of how AI can help in the prevention and treatment of cancer, but every year there is more and more research in this area.

Artificial Intelligence In The Detection Of Rare Diseases

In the field of rare diseases, diagnosis is much more difficult due to their rarity and low prevalence. There are over 6,000 known rare diseases, and the use of AI has been a significant step forward in creating accurate and timely diagnostics.

Machine Learning For Proper Diagnosis

If we look at the numbers, 40% of children with rare congenital diseases receive a first misdiagnosis, and this situation can get repeated several times, even years. Speeding up and improving this method could potentially help over 446 million people worldwide, and AI could help solve this problem.

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

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

Also Read: Nanotechnology In Healthcare And Medicine

Artificial Intelligence For Alzheimer’s Disease Detection

AI is also applied 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 disease, the most common cause of dementia. It is a difficult disease to treat and is easily confused with the common mild cognitive impairment associated with aging. affordable and affordable way than traditional medical tests

Thanks to AI, subtle changes in speech and behavior can be detected faster and more reliably than human observations. The goal is a smartphone app that facilitates diagnoses in a more accessible and affordable way than traditional medical tests. Thanks to NLP, the algorithm can learn the text of patients’ conversations by examining the variety of words that people use to assess their cognitive state and recording time.

This is just the first step—the next step is to use Computer Vision to analyze facial expressions and words spoken by patients in interviews and study the acoustic cues of conversations. The app will assimilate all of this data and then rank users on their risk and likelihood of showing signs of dementia.

The ultimate goal of all these supporting tools is to make the diagnostic process easier for the physician. Providing these tools to the public can also help millions of people identify their illnesses early and seek medical care, speeding up their access to proper treatment and delaying the impact of the disease.

Conclusion

These are just a few examples of what artificial intelligence can do to help the healthcare sector, but there are many more. From simple concepts grow projects that present a challenge to innovate and reimagine any industry thanks to technology.

The healthcare sector has become an environment in which traditional tools are explored and evolved with never-before-seen developments, be it Augmented Reality, Big Data, or AI.

Also Read: Data Science and AI In Healthcare