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Healthcare & Medical

AI Supports Drug Research and Individualized Treatment for Chronic Illness

(616 words)

The demand for innovative medical research to develop cures for chronic illnesses is increasing.

The rapid adoption of artificial intelligence, or AI, is a pharmaceutical trend to watch this year and beyond. The advancing technology will enable medical research to proceed more rapidly and with greater efficiency. 

Machine learning and AI will play a critical role in the future of the pharmaceutical industry, say stakeholders. The most promising applications of these technologies include drug discovery and manufacturing, diagnostic assistance, and optimization of medical treatment processes. 

Other applications of AI include individualized patient assessments. Precision medicine and personalized patient care result in a more accurate diagnosis and help determine the best medication or treatment method for each patient, based on their unique physiology and medical history. 

What is AI?

According to IBM, AI is simply “the science and engineering of making intelligent machines, especially intelligent computer programs.”  

We think of AI as a system that processes information logically, in the manner of a human, then comes to a conclusion. In some cases, action is taken.

AI systems function much like the human brain. Their “thought process” is actually more efficient, since AI systems avoid the distractions which compete for our attention and the preformed opinions which challenge our ability to remain objective.

AI Supports Chronic Disease Research

Chronic diseases are the leading causes of death in the United States. Traditionally, treatment of symptomsnot an actual curewas the only option for victims of several potentially fatal conditions:

  • Chronic kidney disease
  • Cancer
  • Diabetes
  • Idiopathic pulmonary fibrosis

While we still haven’t found cures for these illnesses, there is now hope, thanks to the application of AI technology in the search for new drugs and treatments for chronic health conditions.  

AI and the Selection of Case Study Participants

Plans are in the works for scientists to conduct advanced research in pursuit of innovative cures for many chronic illnesses. AI will increase the probability of success by improving the process of selecting candidates for clinical trials. Scanning patients and identifying those most appropriate as subjects for each specific test will help ensure the most successful results.

AI technology helps eliminate some of the factors associated with test subjects that could impact results. Previously, scientists had to find ways to compensate for such factors themselves, using large trial groups.  

AI Supports the Selection of Individualized Treatment Methods

AI will also be used to more thoroughly screen and diagnose patients. The resulting data can be used to glean additional valuable insights from already existing data. As more data is added to shared systems, the wealth of information will result in better informed patient decisions.

AI-empowered clinician decision-making is suggested by many as the solution to the most serious challenges facing the providing of precision medicine. Without AI, it’s a very time-intensive and error-prone process to consider a number of statistics from a particular patient—genomic analysis, symptoms, history and lifestyle—then to reach a diagnosis and determine the most appropriate drug or method of treatment for that patient. 

AI Supported Research Simulations 

Research trials which use AI for modeling, simulation, and computation, rather than human research subjects, can speed up the research process. This type of research also saves costs. While human subjects are still required for reliable testing of new drugs, AI-facilitated, machine-only research can result in more deeply informed conclusions for initial research phases. 

AI-based preliminary research was vital during the early days of the pandemic when it was difficult to recruit human research participants due to restrictions. Launching research projects with AI-based analysis, and then seeking smaller, more targeted patient populations for late-stage research is a more efficient and less expensive option, so it’s expected to become a prominent pharmaceutical industry trend.

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