‘Data is the new oil’, we can hear this term being thrown around. Generating large amounts of data was never a problem. The difficulty was in making that data organized and available in an actionable format – in shorter time duration. However, the real challenge has been driving meaningful insights from that data – which is being addressed with the help of AI and Machine Learning.
The application of AI has moved past the general applications as we can see it making groundbreaking advances in the pharmaceutical and healthcare industry.
1. Drug Discovery
The COVID pandemic highlighted the need for faster drug development procedures, and the pharma companies delivered. In a global setting where disease transmission is quick, the need to expedite drug discovery procedures is important. Pharma and Biotech companies are leveraging the abilities of AI for rapid drug development. Once the genes and proteins associated with diseases are identified, the cognitive ability of AI allows thousands of chemicals to be screened to recognize potential drugs that affect the disease. The vaccine or drug development that would previously take on an average of 5-10 years has been reduced to 2-3 years with the help of AI and Data Analytics.
2. Digital Biomarkers
Digital biomarkers are making it easier to analyze data and predict or influence healthcare outcomes. As patient data is being increasingly harvested digitally, the opportunities to implement artificial intelligence (AI), especially machine learning, are increasing exponentially. Recently, the FDA issued the emergency use of the first machine learning-based COVID-19 non-diagnostic screening device called the Tiger Tech COVID Plus Monitor. The device recognizes certain biomarkers that may be indicative of SARS-CoV-2 infection as well as other hypercoagulable conditions or hyper-inflammatory states in asymptomatic individuals over the age of 5.
3. Gene Editing
Gene editing has made it increasingly possible to alter DNA sequences in living organisms in turn customizing the gene expression. The main applications of AI in gene editing are the identification of harmful genes and the treatment of diseases. AI reduces concerns regarding human errors with gene editing and is recognized to improve the procedure accuracy and yield better results.
While there are drawbacks to gene editing, AI and Machine Learning have tremendous potential to make gene editing more efficient and accurate. This will influence pharmacogenomics, genetic screening tools for newborns, enhancements to agriculture, and more.
4. Precision Medicine
he role of AI in Precision Medicine development is promising a new future of personalized healthcare. AI-enabled reinforcement learning allows sophisticated computation to generate insights, and empower clinicians to make well-informed decisions. On the other hand ‘precision therapy’ can be coined as a term as well, where we can see AI and ML algorithms being trained according to certain common health conditions, which enable caregivers to outline a therapy for patients suffering from mental health issues based on insights.
Data is critical for drawing insights, and technology is bridging the gap between this data and actionable intel. While the above-given trends are only the tip of the iceberg, AI & ML are expected to dramatically improve health outcomes – whether in the research of new drugs or the delivery of clinical care.