Artificial Intelligence is integrated with our modern life. It is already a part of our everyday technologies such as voice and facial recognition on our smartphones, driving instructions on our automobiles, movie recommendations on entertainment platforms, and many more. AI has been making ground-breaking impacts in every sector, and now it is being used extensively in the biotech industry as well.
AI applications have found their way into biotech and are used for drug target identification, screening of drugs, predictive modeling, and image screening. AI is also being employed to sift through research journals and manage large sets of clinical trial data.
In this article, we will discuss how AI engineers are transforming the biotech industry using AI.
Role of AI engineers in biotech space:
AI is growing steadily in the biopharma industry, which has led to a surge in AI developer jobs in the biotech space.
Here are some ways AI engineers are implementing AI to improve the biopharmaceutical industry.
- Manufacturing process improvement
Artificial Intelligence or AI offers various opportunities to improve development as well as production processes. It can perform quality control, reduce the wastage of resources, minimize the design time, improve reusability, and more. You may read more about the benefits of AI for the pharmaceutical manufacturing industry here.
As a biopharma company, you must hire AI talent who can help design AI algorithms that can make production more efficient with quicker returns and less misuse. These algorithms not only ensure precision in a task but also analyze it to find areas for improvement. Overall, it can prevent human intervention leading to faster production and quality of the product.
- Drug discovery and design
AI is playing a significant role in drug target identification, drug repurposing, and multi-target drug discoveries. The main advantage for pharmaceutical companies is the potential for AI, mainly used during drug trials, to shorten the time it takes for a drug to be approved and released to the market. This can result in significant cost savings, which could translate into lower-cost drugs for patients along with more treatment options.
Artificial Intelligence programmers could develop a model to help pharma researchers validate drug targets for a disease using the data present in EMR records and generation sequencing. They could also aid in designing new molecules to create representative models for individual patients.
- Processing biomedical and clinical data
Most clinical studies still keep their patient logs in paper diaries and note the drugs taken or if they had other adverse reactions from it. All these test results, handwritten notes, image scans, and affecting environmental factors can be gathered and interpreted with the help of AI. This could speed up the research by cross-referencing or combining and extracting data by analysis.
AI algorithms are most functional when designed to read, classify and interpret large volumes of data. AI engineers could convert this function to help with research in life sciences. This can be a significant time-saver for researchers as it provides a more efficient way to examine the massive amounts of data from the growing volume of research publications to validate or reject hypotheses.
- Rare diseases and personalized medicine
AI can surf through thousands of body scans, patient biology, and analytics to gather information to detect ailments in the patients. It can even predict issues the patients might face in the future based on hereditary genetics. There are already systems in place that comb through patients’ medical records and recommend a customized treatment plan based on the information.
AI software development has given way to developing an AI model that could consider the patient’s reactions to past drugs to recommend personalized drug treatments.
- Identifying clinical trial candidates
Aside from assisting in the interpretation of clinical trial data, another application of artificial intelligence in the pharmaceutical industry is in the recruitment of patients to participate in trials.
AI programmers have built systems that can analyze genetic data to identify the appropriate patient population for a trial and determine the optimal sample size using advanced predictive analytics. Some AI technology is capable of reading free-form text entered by patients into clinical trial applications, as well as unstructured data such as doctor’s notes and intake documents.
- Prediction of treatment results
Now, AI models can associate drug treatments with individual patients who have largely reduced the room for error. This has cut costs as well as saved time while treating high-risk patients.
AI programming has made it possible to predict a patient’s reaction to a particular drug. It analyses all the contributing factors that can affect the results, such as compound absorption by the body, distribution of the compounds, and the patient’s metabolism.
- Drug repurposing
The promise of drug repurposing is one of the biggest respites for pharma companies pressed for a budget. AI engineers play a significant role in delivering great value to these companies.
Drug repurposing is something that most biopharmaceutical companies desire as it reduces the risk of unprecedented toxicity or side effects during human trials and less expenditure.
- Drug adherence and dosage
The participants in the clinical trials must at all times abide by the drug study protocol. Those who do not follow these rules are generally removed from the study at the risk of tainting the drug study results. Else this could cause a hugely adverse effect on the results of the trials.
One of the most important aspects of a successful drug trial is ensuring that participants take the prescribed dosage of the studied drug on time. That is why having a method to ensure drug adherence is critical.
AI engineers can develop remote monitoring algorithms that separate the good participants from the rest and evaluate the test results accordingly.
Artificial intelligence is being used extensively in the biotech industry, including clinical data management, disease prediction and prevention automation, and biomarker discovery. AI is opening up new opportunities for a future generation of healthcare and biopharma solutions capable of outperforming the analytical abilities of the best human minds.
However, despite all of AI’s benefits to the pharma industry, surveys show that less than 5% of healthcare facilities are currently using or investing in AI technologies.
Most companies have outdated infrastructure that is not capable of supporting AI systems. They lack interoperability as well as proper data storage. So, to transform those facilities into fully operable and become one of the most influential companies in the world AI structures, talented AI engineers are required. Though AI engineer jobs have become abundant, the hired developers must collaborate with healthcare professionals to seamlessly process the data effectively.
Vedasree is a content writer who spends her time learning new things and having fun while doing them. You can find her in a quiet corner with her head buried in a book when she is not writing!
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