Precision Algorithms in Healthcare: Improving treatments with AI
June 3, 2021
5 min read
It’s 2020 and we can safely say that the year hasn’t been our best or what we wanted it to be like. The alarming spread of COVID-19, and its aftermath has people unrooted and shaken to their toes, and literally everyone is looking at technology and healthcare innovations to find an answer to the pandemic. And fast.
Precision medicine developed with AI algorithms is one such promising solution. The aim of developing precision medicine is to design and optimize diagnosis, therapeutic intervention, and prognosis with the use of multi-dimensional data, factoring in genetic makeup, environment, and other data. Currently, precision medicine is used to treat lung cancer, colon cancer, HIV, rare childhood illnesses and the like.
To understand the impact of precision algorithms in healthcare, let us understand what precision therapy is, how it benefits us, and how they are being used to deliver better preventative and curative treatment.
What is precision healthcare?
Recent advances in data science are providing better insights into genetic information and electronic health records (EHRs) giving doctors a better idea of the individual patient and treatments tailored to meet their specific needs. AI algorithms can provide a deep understanding of patients, and can even predict how patients might respond to a certain treatment.
AI algorithms in healthcare are being used to develop precision treatments for complex diseases. By obtaining information from disparate sources such as EHRs, environment, genetic history, sensor history, it can provide insights into what makes patients healthy at an individual level. With these insights, preventative and curative care can reach new heights. Such insights could further be used for the discovery of new drugs, using old drugs for new treatments and other cases.
Such targeted care is often referred to as precision healthcare/precision therapy/precision medicine or personalized medicine – drugs or treatments designed for small groups, based on characteristics such as medical history, genetic makeup, and other data, at times, even provided by AI-powered wearable applications.
Employing AI-powered Algorithms in Healthcare:
The use of artificial intelligence can help doctors, patients as well as administrative staff. In Verizon’s 2019 Data Breach Investigations Report, the healthcare industry was the only industry in which there were more internal breaches than external breaches. Also, as per information released by data security solutions firm Egress, 4,856 data breaches took place between January 2019 – June 2019 with the healthcare sector being most affected. This raises questions on the role of AI in cybersecurity and how, when properly implemented AI solutions can provide the required defense mechanism.
AI-backed algorithms for clinical decision support for oncology:
Cancer is an illness like no other. Patients go through pain, anxiety, stress, depression, the list is endless.To top it, there is the mounting expense on chemotherapy.
Till date, a major challenge to developing deep learning algorithms was access to integrated large volumes of datasets as they are with private institutions.As deep learning algorithms require huge amounts of data, development was not feasible.Another challenge in the current scenario is no real-time tracking is possible.Physicians meet and discuss treatments and patient status, however, it is inefficient as cancers tend to mutate, grow, evolve and change. Cancers not only transform itself but also its DNA, making previously planned treatments ineffective.
For addressing these challenges, GE Healthcare and Roche Diagnostics are developing an application of AI in healthcare with the help of medical imaging and diagnostic experts, the support platform built for oncology. The platform will combine radiological imaging, i.e. process the tumor at an anatomical level and a physiological level, provide information back from the laboratory at a molecular level and have access to the latest information and research, helping the physicians address issues in real-time.
The emergence of precision algorithms is going to change a lot of things related to the treatment of cancer. The lowered costs of sequencing have made it possible to identify genetic variants that have impacted health and disease such as the types of genetic changes or mutations that have occurred. While using machine learning in business operations is commonplace, however, Personal Genome Diagnostics, has developed a method using machine learning algorithms that automates the tumor diagnostic process and improves the accuracy of identifying mutations in cancerous tissues. Bearing that result in mind, the doctor can choose the specified targeted treatment for the patient.
Using Smart algorithms for diagnostic accuracy for non-pediatric specialists:
Oftentimes, the brain development of a child is natural, however, appears to be an anomaly. However, most children’s hospitals do not provide pediatric imaging and neither do they have expertise in understanding the developing brain.In such circumstances, non-pediatric specialists are at a loss.
In another instance of game-changing AI ideas for healthcare and precision medicine, Boston Children’s Hospital and GE Healthcare are developing a platform for improved brain scan technology for better, and more effective diagnosis.
The AI and cloud-based support platform will host reference scans helping identify conditions that are a concern from the natural changes of the development of the brain. The machine learning algorithms will help identify previous images that are similar and provide real-time contextual information helping non-pediatric specialists effectively diagnose children.
Precision algorithms for predicting heart attacks:
Precision algorithms will probably be one of the most important developments in the healthcare sector. Not only can it help with oncology and brain scanning, the use of top ai algorithms for healthcare, can also be used in identifying patients who might be at risk of having a heart attack in the near future.
Researchers at the University of Nottingham scanned routine medical data of patients and predicted which patients would have heart attacks within the next ten years. The AI-based algorithm correctly predicted the outcomes for 355 more patients as compared to the standard method of prediction by evaluating risk factors such as smoking, diabetes, cholesterol, and high blood pressure. The algorithm is expected to be in clinical use in the next five years.
Machine learning algorithms for predicting suicide risk:
Machine learning algorithms have been adopted across several sectors. Whether it be for personalized streaming recommendations or for intelligent Customer Relationship Management, it has found widespread acceptance. With the likes of IBM Watson, AI had already found a place in the healthcare industry. Though it has been discontinued, it has not deterred practitioners from developing A.I. systems for identifying, detecting and treating other disorders.
Thanks to that, there is some good news! machine-learning algorithms in healthcare are being used to address a hugely important concern: depressive behavior and suicide risk. Researchers at Vanderbilt University Medical Centre have developed a machine-learning algorithm that can predict the likelihood of an individual taking her/his life with inputs such as hospital admissions data, demographic data, and diagnostic history. With inputs from over 5000 patients, it was able to accurately predict whether an individual would risk taking her/his life in the following week in 84% of cases and was also able to accurately predict whether an individual would attempt suicide in the next two years in 80% of cases.
AI and healthcare
Today, a huge number of deaths happen due to the wrong diagnosis. With artificial intelligence algorithms in healthcare, improved medical care and new standards of safety can be expected. With the emergence of precision algorithms, personalization with AI is bound to reach new levels. Although it is not possible to state when exactly it will be a worldwide practice to use precision algorithms and how the future will be, it is definitely a future we all will be looking forward to. Top AI startups, enterprises like General Electric and medical institutes are all working towards making the world a safer place with AI.
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