Even though the term “artificial intelligence” isn’t clear, most people understand it to mean machine learning that can do tasks that usually require human intelligence, like seeing, hearing, making decisions, and translating languages. Most AI implementations today are narrow in scope, solving only a few problems.
Artificial intelligence (AI) is typically used in the medical research industry for pattern recognition and data analysis in massive datasets. Significantly faster, more accurate, and more cost-effective than standard analytical procedures, this data can be used to cut costs and boost results.
Similarly, AI may be used to combine disparate datasets and sift through mountains of scientific literature to uncover studies with practical use. According to the World Health Organization, cancer is the second leading cause of death worldwide and accounts for nearly 10 million deaths annually. The AI system will be able to apply machine-learning techniques to identify promising new cancer drugs once it has compiled and “translated” this massive amount of data into a common language. Such advances could make a significant impact on cancer treatments and even reduce cancer mortality rates.
As stated by the U.S. Food and Drug Administration, there are five steps to the usual drug discovery approach
It all starts with research in the lab. As a next step, the potential candidate undergoes a series of tests before entering clinical trials and testing in humans. The next step is a decision by the FDA on whether or not to approve the medicine. Once a drug or medical device is on the market, the FDA keeps a close eye on it to ensure it is safe for use.
Step 1: Discovery and Development
Research for a new drug begins in the laboratory.
Step 2: Preclinical Research
Drugs undergo laboratory and animal testing to answer basic questions about safety.
Step 3: Clinical Research
Drugs are tested on people to make sure they are safe and effective.
Step 4: FDA Review
FDA review teams thoroughly examine all of the submitted data related to the drug or device and make a decision to approve or not to approve it.
Step 5: FDA Post-Market Safety Monitoring
FDA monitors all drug and device safety once products are available for use by the public.
AI can be used in the first three phases of drug development by companies that use it to speed up and cut costs of research and discovery
This article summarizes the most significant applications of AI across the drug development life cycle.
Possible Drug Discovery Process:
The preliminary phases of a drug discovery project are crucial. Possible applicants are screened at this point. In the early stages of drug discovery, high throughput screening (HTS) is the most common screening method. Rapid testing of large libraries of compounds is made possible by integrating robotics and automation.
How scientists find a chemical that helps patients:
1. By gathering fresh information regarding disease mechanisms
2. Molecular testing
3. Investigating the unintended consequences of current therapies
4. Using advanced methods of technology to handle genetic engineering
It’s at this point that AI can help out. Scientists may use artificial intelligence to sift through the millions of compounds in their databases far more quickly, efficiently, and accurately than is possible with human scientists alone.
Because of this, a less synthetic effort is required to identify molecules with medicinal potential.
Patient Recruitment
The trial’s overall success depends on recruiting the correct patient population. It is the research team’s responsibility to select participants with the highest probability of benefiting from the treatment being examined. Modern healthcare IT uses artificial intelligence to help find patients and locations for trials.
Mainly, researchers use AI and ML algorithms to identify venues with the most extensive recruitment pool and to propose recruitment techniques. The entire selection procedure is bolstered by artificial intelligence and data analytics, from demographic mapping to patient potential prediction.
Patients are matched with trials with the help of automated technologies that assess medical records and real-world evidence.
Patients Withdraw
Approximately 30% to 40% of participants will withdraw from a study at some point. Contrarily, after five months, 40% of patients are still non-adherent, which results in extra expenses for the pharmaceutical sector. The burdensome nature of patient reporting contributes to high attrition rates. Day-to-day data collection includes recording medication taken, body functioning, and other responses.
Regardless, developments in AI are assisting in enhancing patient retention and medicine compliance. When devices like smartwatches, sensors, fitness trackers or monitors and algorithms are used together, patient data can be automatically read and interpreted.
Clinical trial researchers can then receive immediate results from these deep learning models’ data analyses. Dropout risk prediction is another area where predictive analytics vendors can produce insightful models.
Real World Evidence
The field of RWE analytics examines the usefulness of drugs and medical procedures in actual clinical settings. The pharmaceutical business places a premium on this phenomenon because of its unrealized potential.
This vast trove of information in the medical field serves as a data blanket that can be used for a wide range of analytical purposes. New research paradigms for precision medicine can also be gained using RWE analytics.
Large data sets, including patient outcomes, collected through insurance claims, electronic health records, and other sources are essential for RWE’s cutting-edge analytics.
This information is then utilized to weigh the social benefits of medicine alongside its therapeutic benefits. Data analytics based on real-world evidence is widely recognized as an instrumental component of the drug development process, contributing to the safety and efficacy of treatments while keeping costs down.
Understanding the peculiarities of various patient populations is also a key takeaway from real-world evidence strategy and analytics.
However, tons of computational resources are needed to convert raw data into knowledge to develop real-world evidence. When this occurs, we can rely on artificial intelligence.
Machine learning is a cornerstone of RWE implementation. Intelligent algorithms can be used to generate crucial information to tackle problems that would otherwise take too much time and money to fix.
In particular, machine learning offers novel possibilities for consolidating disparate data sets into a single, authoritative repository. Analytics models can be executed constantly and at scale by tapping into the connected data environments via algorithms.
Trial Data Analysis
Researchers face massive amounts of operational data during study wrap-up. However, the vast majority of insights are hidden by data silos and isolated data architectures. For instance, developing a global trial portfolio takes a lot of work. Dispersed geo-based data is stored in isolated silos on local computers, making it infeasible to put into any analysis rapidly.
Analytics tools with artificial intelligence capabilities can centralize all relevant data and metrics. Algorithms do data cleansing and analysis on trial data and reporting tools display the results graphically to aid decision-making.
The new drug is sent to the FDA for a complete evaluation after undergoing extensive testing and development to ensure its optimal efficacy and safety. The results of clinical trials are reviewed by the FDA, which then decides whether or not to approve the medication.