A machine recommending a song or updating you on today’s weather is only a glimpse of what Artificial Intelligence (AI) and Machine Learning (ML) can do. The impact of data technologies goes beyond that and across various facets of human lives. Science and research are not left untouched too.
Many of the biggest names from the technology industry have invested heavily in AI and other data technologies. Google, Microsoft, IBM, and Samsung etc., have each submitted thousands of patent applications on AI. The global pandemic caused by COVID-19 has only pushed the momentum forward as companies go digital and require advanced data capabilities to process huge piles of data generated from various sources and make sense from it.
Science and research experiments produce an avalanche of data and information and it is beyond the human mind to keep up with it. And therefore, many scientists and research teams are leveraging Artificial Intelligence and Machine Learning algorithms for help. Today’s advanced AI systems can mimic the human mind and plough terra bytes of data, detect patterns and highlight anomalies to deliver actionable insights to scientists.
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Let’s look at some of the applications and use cases of AI and ML in major fields of science.
- Medical Science: AI is being used in medical research to analyze patterns to detect critical diseases like cancer, heart attack etc. AI is also helping researchers in analyzing millions of Electronic Health Records (EHRs) and other patient data to derive scientific insights. Medical imaging analysis and disease diagnosis are getting automated to help experts cover more cases and speed up the process to save lives.
Interpreting X-rays, CT, and MRI scans are highly skilled jobs requiring many years of training. Demand for qualified radiologists is high and the trend is likely to continue as healthcare evolves. AI can help radiologists quickly reading images and diagnosing more cases. Let’s take an example of X-ray diagnosis. An AI system can be built that uses historical data, Convolutional Neural Network architecture and heat map visualizations to improve diagnosis speed and accuracy.
You need to upload an X-ray image onto the software. The Convolution Neural Network algorithm then examines the X-ray image and identifies the probability of pathologies like Atelectasis, Cardiomegaly, Consolidation and Pneumonia etc., along with a heatmap image localizing the affected areas. The radiologist can then look at the high probability cases and confirm the diagnosis.
- Research: Scientists have to regularly go through volumes of reference material, books and journals to find information specific to their projects. Doing this manually takes hundreds of hours. AI can help them in mining the entire digital libraries and provide them with specific journals and books that contain the desired information in a few minutes.
According to an article on nature.com, when computer scientist Christian Berger’s team while working on its project about self-driving vehicle algorithms, found an overwhelming more than 10,000 papers on the topic in a systematic literature review. Investigating them manually would have taken a year. The team used an AI-driven literature-exploration tool. The tool reviewed a 300-to-500-word description of a researcher’s problem, or the URL of the papers, and provided a map of matching documents, visually grouped by topic. This helped the team get a quick and precise overview of identifying documents relevant to a certain research question. What could have taken a year is done in a matter of hours.
- Genomics: AI is playing a big role in the study of genomics. Recently, researchers from the University of California – San Diego leveraged AI technologies to discover a DNA code that could help in controlling gene activation. Researchers are also using AI to study trillions of gene data which could have taken thousands of years. This is helping scientists in accelerating their studies and paving way for new findings and discoveries.
Deep Genomics leverages AI and Machine Learning to interpret genetic variation, patterns of single nucleotide polymorphism (SNPs), and to understand the impact of the variations on cellular processes including DNA repair and metabolism. Cancer Genomics is helping researchers use cancer sequencing data to create personalized treatment plans for patients. In this, the DNA fingerprint of a tumor is analyzed to create personalized treatment plans. Liquid biopsies are used to understand the effectiveness of the treatment plan and the growth of the tumor.
- Research Assistance: AI-driven robots are being used in labs as research assistants to do tasks that require optimal accuracy like measuring liquids and mixing explosive liquids. Cameras and sensors fitted in the robots are collecting data, analyzing it in real-time and providing insights to scientists.
- Astronomy: Scientists at the University of California have developed powerful computer software named ‘Morpheus‘ that analyzes astronomical image data pixel by pixel to identify and classify all of the galaxies and stars from astronomy surveys. The software is a deep learning framework that leverages AI technologies for image and speech recognition. The increasing size of data sets is necessitating automation of tasks and this is helping scientists and researchers in doing more in less time.
AI and ML have the potential to play a transformative role in science and research. The early-stage applications of AI are resulting in scientists saving years of labor and analyzing volumes of complex datasets that are beyond human capabilities. This is accelerating studies and what was earlier possible in years is now being achieved in months.