Artificial intelligence (AI) and machine learning (ML) have completely transformed every aspect of our life. It has promised to streamline processes, bring efficiency to the workplace, and revolutionize how we operate. AI and ML show promising growth in the life sciences, healthcare, and pharmaceutical industries. However, some are still skeptical about the efficacy of such tech and use it restrictively, limiting its potential. However, clinical machine learning coupled with EDC can spawn tremendous value in the future. Let’s explore how.
Even though the clinical research, pharmaceutical, and healthcare aspects have seen an evolution with the introduction of new technology such as EDC systems, eCRFs, and patient registries, there is still room for improvement. AI and ML can work with modern EDC systems for clinical trials by learning from vast data, detecting hidden patterns, and providing robust and personalized solutions.
However, significant hurdles and cross-industry challenges prevent the implementation of ML and data science in the pharma industry. Let’s have a look at some of those in detail.
Clarity
There is a considerable lack of clarity in determining how ML, AI, and data science can improve pharmaceutical processes and therapeutic aid development. The pharma industry has always relied on traditional processes and one-off clinical studies. Traditional statistics are used where averages are used to determine outcomes. These statistics are not an adequate representation of the entire population, leading to ineffective medicinal development.
AI and ML have the potential to transform this aspect. It can allow you to incorporate the entire population rather than a select few. Pharmaceutical companies need to realize this potential and incorporate this into their decision-making processes.
Machine Learning and Artificial Intelligence Technical Challenges
There are numerous technical challenges that pharma companies need to overcome before using ML and data science in their operations. The lack of integration and access to data presents a significant roadblock. In saying that, EDC systems are proving to be a game-changer in this aspect as it makes data collection, storage, and analysis relatively straightforward.
Pharma companies also need to hire technical and qualified data scientists to create unique algorithms to run the process effectively. They will also provide significant expertise and knowledge on the analysis side of things. All critical stakeholders involved in the industry should encourage cross-collaboration to improve the data aspect of their operations. Data science and ML can completely change the way pharma companies operate.
Get Started with ClinicalPURSUIT
If you run clinical trials and are looking to integrate robust and effective EDC systems, check out the products and services offered by ClinicalPURSUIT.
We are changing the clinical research landscape by providing effective Clinical Trial Data Management System solutions and Clinical Trial Data Management Software. We are pioneers in providing quality CTMS solutions that’ll help you automate your processes, provide real-time reports, and help you collaborate with your peers.
We also offer various other services such as Randomization and Drug Supply Management, Electronic Patient-Reported Outcome Software, etc. You can visit our website for more information or schedule a one-on-one free demo today!
