Artificial intelligence has a huge potential for being a game-changer in the biomedical field. The technology has helped solve biological enigmas extending to both diagnostic and therapeutic applications. Pharmaceutical giants like Novartis, AstraZeneca have all realized this potential is setting up their collaborations and centers in hope of groundbreaking discoveries.
Today several pharmaceutical companies are investing in Artificial intelligence labs that are depending on machine learning algorithms to revolutionize drug design and discovery. Conventionally drug discovery takes several years of testing molecules that undergo pre-clinical testing. A typical drug takes 15 years to reach the market with an investment of more than 2 million dollars. AI can cut short this time by validating novel molecules within days. For instance, Johnson & Johnson recently invested in an Artificial intelligence company, BlackThorn for developing drugs for psychiatric disorders. Another biotech company Insilico Medicine has been expanding the technology for therapeutic applications in cancer, immunology, and others. The process has accelerated from years to weeks and this can immensely benefit the healthcare sector by bringing life-saving molecules in the market.
Researchers today are deploying AI for advancing the field of functional and structural genomics. Google’s sister company DeepMind is a perfect example of the exploitation of the technology on a large scale for using a deep learning algorithm that uses high throughput sequencing to reconstruct the genome sequences. It can also predict the physical structure of proteins thus determining how drugs will bind to it. In fact, in a conference held earlier this year, DeepMind had beat a group of biologists in predicting the protein’s structure based on genetic code. Another highly potential field is using Ai for gene editing applications. A team of computational biologists and scientists collaborated recently for improving the efficiency and accuracy of the CRISPR tool to avoid off-targeting.
Diagnosis and prediction of malignancy are often inaccurate during early stages un till the disease has progressed very far. By analyzing the MRI scans and 3D volumetric data, AI can replace radiologists in cancer diagnosis. It can be used as an assistive methodology by oncologists in strategizing the line of treatment. In diseases like Alzheimer’s neurodegeneration begins to occur years before the actual onset of the disease. By using predictive models on neuroimaging data, AI can specialize in Alzheimer’s diagnosis much before. A team at the University of California created an AI tool that was trained on millions of child medical records. It could predict the incidences of childhood disorders as accurately as a pediatrician.
Although much of the work was focused on predictive models, AI has extensive potential for therapeutic function as well. Prosthetic limbs are being studied to perform advanced neurological functions. Robotic devices are being constructed to mimic human capabilities. In fact, artificial synaptic devices are being designed to perform a function similar to neurons. This can benefit individuals with paralyzing neurological disorders.