Benefits Of Artificial Intelligence And Machine Learning In Clinical Trials For Drug Development

3 min readMar 25, 2022


Data illustrations by Story set

Artificial intelligence (AI) and machine learning (ML) are the talks of the 21st-century technological landscape.

The applications of these tools are spreading across various industries such as automotive, healthcare, and much more. However, the drug development industry can benefit significantly from AI and ML in clinical trials.

Here are a few ways how.

1. Identifying Patients For Clinical Trials

Drug development depends on patient testing through clinical trials. However, identifying patients for such trials can be a complex process. AI and ML can change the game for this process as they will allow software systems to mine large datasets of patient information for identifying relevant candidates.

Many companies are also using the power of AI to implement predictive models. These enhance the statistical importance of data collection from targeted candidates. It also helps to reduce the cost of clinical trials in the long run.

2. Reducing Costs Through A Synthetic Dataset

A synthetic dataset is based on a real-world dataset used to predict the synthetic patient profile and deploy a virtual unit in clinical trials. Traditional clinical trials require billions of dollars in funding, time, and many other resources. Besides that, recruiting patients can also be challenging, which means treatments will take longer to hit the market.

On the other hand, a synthetic dataset using AI and ML can help overcome these challenges. Such a dataset will reduce patient numbers and eliminate any placebo to accelerate the clinical trial process for drug development. Synthesizing patient characteristics can also optimize the protocol design.

3. Gaining Insight Through Engaging Patients

Clinical trials for drug development using AI can keep patients engaged. That is because utilizing technologies such as fitness trackers and digital reporting applications can lead to real-time communication and engagement. Patients can also send feedback on their symptoms and share other relevant information with the researchers.

In the long run, this will help eliminate the need for patients to travel to the sites, increasing research compliance. It also allows for monitoring objective and high-quality data as it is collected in real-time while the patients live their lives. So, AI can help researchers gain insight while developing new drugs.

4. Helps Assess The Impact Of Various Dosing Regimens

Finally, new AI tools can help drug manufacturers solve issues related to optimal doses and drug administration. For example, customized AI algorithms can determine the impact of various dosages and drug schedules. That is especially true for combination therapies on drug safety and efficacy.

Such AI tools will help reduce the risk of clinical trial delays, unfavorable events, and patient discontinuation from clinical trials. The data rules for such algorithms are customized to specific patient populations by witnessing how individual patients respond to treatment. Besides that, it also observes the impact of dosage adjustments on side effects and drug efficacy.

Final Words

These are the top four benefits the healthcare industry can reap from AI and ML in clinical trials for drug development. Such tools have the power to transform the drug manufacturing industry in no time and they are surely playing their part in doing so.




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