Artificial Intelligence (AI) and machine learning (ML) has turned out to be omnipresent in tech startups, fueled to a great extent by the expanding accessibility, measure of amount of information to be processed and less expensive, yet more powerful computers. In the course of recent years, AI and ML has been able to enrich the biotech domain due to analogous transformation of biotech information.

A survey of pharma and life sciences experts showed that 44% were using AI in their R&D activities. The survey also revealed that AI majorly finds applications in preclinical phases of drug development.

Here are a few applications of AI in Biotechnology-

1.   Drug Discovery & Clinical Trials

Drug discovery has been the most exciting application of AI and ML. Organizations are adopting a structure-based approach for drug discovery, utilizing ML to discover small molecules that could give therapeutic benefits dependent on known target structures. Majority of AI use-cases and rising technologies for clinical trial seem to revolve around three essential applications: patient recruitment, clinical trial design and its optimization.

2.   Diagnostics

Machine learning and AI are being used in the diagnosis of cancers. With Quest Diagnostics, IBM came up with IBM Watson Genomics, which uses machine learning to make cancer identification more precise. The other ML applications include pathology and in rare disease diagnosis. A recent study has shown ML being more accurate than cardiologists in detecting heart diseases.

3.   Radiotherapy and Radiology

AI has proved to be helpful in reducing the radiation therapy planning process to just minutes, thus saving radiologists several days and improving patient care. DeepMind Health with University College London Hospital is creating machine learning calculations to build the precision of radiotherapy arranging by separating sound tissues from malignant ones.

4.   Personalized Medicine

There is much research continuing in regards to the utilization of machine learning and prescient examination in redoing treatment to an individual’s special wellbeing history. In the event that fruitful, this can result in streamlined findings and treatment conventions. As of now, the emphasis is on directed realizing where specialists can utilize hereditary data and side effects to limit analytic alternatives or make an informed conjecture about a patient’s hazard.

5.   Gene Editing

Complicated tasks such as designing constructs for gene editing are being taken care by AI programs as assistance providers. Desktop Genetics has created a platform to design gene editing constructs using CRISPR that works through AI. Their gene editing platform runs the entire process from RNA selection to data analysis.

6.   Electronic Health Record (EHR)

Evidence based medicine and clinical decision support systems designed on the machine learning platform have capability of making an EHR system more powerful and will help doctors in making informed clinical decisions specific to a patient’s preferences and clinical history. Medical records can also be effectively managed through AI and digital automation. The huge amount of data can be efficiently stored, formatted and accessed for better patient care.

7.   Medication Management

Mobile apps are being developed to monitor the medication program of patients. The smartphone webcam is connected to AI to manage prescriptions of patients. These can be useful for patients with chronic illness and clinical trial participants.

8.   Sales Rep Performance

Machine learning will remove cumbersome data entry and give a virtual assistant to arrange drug stores—all through a representative’s cell phone. AI can also help companies and representatives in customer segmentation that could aid in effectively targeting potential physicians.

We, at GPUONCLOUD, are working on one such area dealing with AI based Skin Cancer classification for social cause to extend health benefits to all including rural population. Stay tuned to our updates on social media!