In recent years, the use of Artificial Intelligence (AI) in the pharmaceutical and biomedical industries has shifted from science fiction to science fiction. The next evolution of this advanced data analysis approach includes artificial intelligence and machine learning.
Most of the artificial intelligence solutions used in healthcare today are based on artificial data science algorithms. This type of artificial intelligence uses multivariate data analysis supported by past experience. For example, it can combine patient outcomes with clinical data and individual patient medical histories to create treatment alternatives and recommend drug combinations.
The next level of AI is deep learning, which is also based on neural networks but includes a combination of separate computing layers and combined signals. Thorough education has great potential for diagnostic applications, being able to precisely analyze images (eg images of skin diseases or X-rays) in combination with pathological data and historical treatment outcomes.
Application of artificial intelligence in the pharmacy
From the early stages of drug discovery to prescribing treatment options, the use of AI in the biopharmaceutical industry continues to grow, with projected market size of $10 billion by 2024 (including medical imaging, diagnostics, personal AI assistants). genomics).
For example, pharmaceutical researchers can identify and validate new cancer drug targets by using data such as longitudinal electronic medical records (EMR records), next-generation sequences, and other "omic data" to create a representative model of each patient.
A Cognizant study showed that approximately 80% of clinical trials missed enrollment deadlines, and one-third of all phase III dropouts were due to difficulties in enrolling.
Rare Diseases and Personal Medicine
By combining information from body scans, patient biology, and analysis, AI is used in a variety of ways to detect diseases such as cancer and even predict health problems people may face based on their genetics. One example is IBM Watson for Oncology, which recommends personalized treatment plans based on each patient's medical records and history.
AI is also used to develop personalized drug treatments based on individual test results, past drug responses, and patient historical data for drug responses.
A staggering 86% of clinical trials failed to recruit enough patients. This results in slower research and slows down patient access to life-saving drugs.
Turning to AI in pharmaceutical development
While the possible uses of AI in pharmaceutical and biotechnology development are evident, actual progress in adopting such technologies could be slow. For example, when social media adds AI to your photos, you will get instant feedback on whether the results are correct or not so that the AI can learn quickly. Once an active ingredient is discovered, it can take months or years for feedback for a new molecule to be confirmed as an active ingredient.
However, there is no denying that AI will be the next big thing in the pharmaceutical industry, and companies that adapt and embrace new processes will have a strategic advantage. A good starting point is the use of current data analysis technologies based on multivariate and predictive analysis.
