Artificial Intelligence in Pharmaceutical Industry
If the last two years have proved anything, it’s that the pharma industry has to be ready to act fast in times of crisis. The rise of machine learning and artificial intelligence in the pharmaceutical industry has accelerated the pace of drug discovery. But the question remains: How can we safely accelerate the development and approval process for novel pharmaceutical technology?
In this article, we explore the increasingly critical role that artificial intelligence (AI) and machine learning (ML) play in the pharmaceutical industry, from predictive forecasting and preventative therapeutics to clinical trials and drug discovery.
Artificial Intelligence Pharmaceutical Applications
The application of artificial intelligence to pharmacy and medicine represents one of the most cutting-edge scientific endeavors in recent history. By 2025, it’s predicted that roughly half of all pharmaceutical companies will have implemented some measure of AI technology. Below, we highlight nine current AI applications in healthcare.
1. Drug Discovery and Manufacturing
By historic standards, the average vaccine development time is 10-15 years. The mumps vaccine actually set the speed record at just 4 years. But COVID-19 was a wake-up call. It proved that, in the midst of a pandemic, time is a luxury we can’t always afford. Within just three months of the first outbreak, COVID vaccines had already entered human trials. But how?
Researchers and developers leveraged the combined power of big data, machine learning, and computation analyses. Designing an effective vaccine requires a thorough understanding of the virus itself, from the structure of the small molecule to its actions in the human body.
AI technologies made it possible to visualize, in detail, all of this key data. Additionally, sophisticated predictive models provided insight into likely genetic mutations; this helped anticipate future stages of vaccine development.
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2. Clinical Trials
According to a study conducted by Deep 6 AI, about 86% of clinical trials fail to recruit enough patients. This is because most studies acquire participants through doctor-patient referrals, leaving the process to chance and circumstance.
With new advances in AI, pertinent patient data can be proactively mined from both structured and unstructured sources. By parsing through enormous digital archives, AI can make accurate patient recommendations based on a thorough examination of medical history, demographic information, and other key criteria.
3. Diagnosis and Disease Identification
Sophisticated AI algorithms can now analyze diagnostic medical images. By detecting subtle changes and pathological patterns in medical scans, diseases can be identified at an earlier stage than ever before. This allows for rapid and effective intervention that would not be possible with the human eye alone.
At this time, implantable diagnostic technologies are also being developed. The aim is to provide a continuous, real-time feed of health data. By recording and monitoring baseline physiological and biological patterns over time, faint changes can be detected. Wearers and physicians alike are then alerted of these changes and medical recommendations are made.
4. Disease Forecasting
Epidemiology is a cornerstone of public health. It is the scientific study of disease distribution, patterns, and causes across a population. Epidemiologists are employing ML and AI technologies to predict and monitor epidemics across the world—from seasonal colds to novel viruses.
Predictive forecasting has far-reaching consequences. When done correctly, it enables the timely and appropriate delivery of preventative healthcare. It also preempts necessary supply chain adjustments to ensure medical inventory is available in the right place at the right time.
5. Digital Medicine
Digital medicine is a subcategory of digital health. It comprises a host of electronic devices that measure and collect health data on behalf of a unified system, such as electronic medical records.
Sophisticated wearables measure internal health indicators like blood pressure, blood oxygen, core temperature, breathing rate, and even electrical activity throughout the brain, muscles, heart, and skin.
They can also measure environmental factors like GPS coordinates, direction of travel, elevation, physical movement, and sounds. Self-report data including diet, wellbeing, and lifestyle can also be collected.
These AI-driven devices are used to anticipate, manage, and even treat illnesses and disorders. Examples include blood glucose sensors, heart-rate monitors, and other wearables.
6. Digital Therapeutics
Digital therapeutics (DTx) is a further subcategory of digital medicine. DTx uses AI and other technologies to spur positive behavioral changes in patients. In many use cases, patients have shown preliminary or early-stage symptoms of diseases like type II diabetes, heart failure, obesity, and substance abuse.
By using digital products to administer cognitive behavioral therapy (CBT), it’s become possible to improve long-term health outcomes. Moreover, DTx technologies are delivering increasingly personalized patient care, providing users with custom, evidence-based therapeutic interventions.
7. Cancer Research
AI has become an effective tool in cancer research and cancer drug development. A pharma company equipped with this technology can effectively adjust their development strategies to reflect new evidence. For instance, advanced algorithms can anticipate the way in which cancer cells become resistant to existing drugs.
AI is also being used for a variety of other cancer-related applications, such as to identify tumor neoantigens and improve tumor immunotherapy; to map radiation treatment plans with increased precision; to manage chemotherapy based on unique patient tolerance and treatment efficacy; and to generally improve clinical decision making (CDM) among oncologists and radiologists.
8. Natural Language Processing (NLP)
Language is one of our most fundamental cognitive abilities as humans; however, natural language processing (NLP) represents one of the greatest hurdles in AI research. Part of the difficulty lies in trying to parse large quantities of unstructured data.
AI is helping to create NLP systems that recognize human speech and writing, while also being able to adapt to unique speech patterns and evolving lexicons. Meanwhile, natural language understanding (NLU) aims to cultivate linguistic comprehension that goes beyond literal meaning.
NLP already exists in a variety of forms, from voice-controlled assistants (such as Alexa and Siri) to writing tools (like Grammarly and predictive text). But NLP is also being used in healthcare to extract actionable intelligence from unstructured data sources, such as PDFs, faxes, voice memos, and so on. This helps streamline workflows, organize information, minimize human error, and increase data accuracy.
9. Computer Vision
Computer vision (CV) is a field within AI that promises many innovative applications. By learning to “see” and interpret visual information, CV algorithms can detect objects, classify images, identify anomalies, and make recommendations.
Self-driving cars use a form of CV to detect pedestrians, assess traffic, find parking, and more. In healthcare, computer vision applications are largely related to the video and digital imagery produced by medical scans.
For instance, by using CV technology in conjunction with X-rays, subtle disease indicators can be discovered. CT and MRI scans also employ deep learning CV to detect a variety of medical issues including internal bleeding, clogged blood vessels, tumors, and more.
Power Your Digital Transformation, With Ideas2IT’s Artificial Intelligence Pharmacy Applications
The intersection of artificial intelligence and pharmacy offers a host of exciting possibilities. Ultimately, the goal is to facilitate and improve clinical decision-making. Already, the use of artificial intelligence in pharmacy has enabled healthcare companies to improve every facet of their business, from R&D to patient outcomes.
Looking to drive your own organization’s digital transformation? Contact us today to leverage custom software solutions using this emerging technology.