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Artificial intelligence and machine learning are used in medical trials to boost human productivity rather than replace humans with digital technologies.
Fremont, CA: Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) were becoming a cornerstone of successful modern clinical trials and got integrated into most of the technologies allowing clinical development transformation.
In recent years, the health and life sciences business has made a major leap into the digital era, with innovations and scientific advances boosting patient outcomes and public health. As a result, adopting digital transformation is no longer a choice but rather an industry norm. Let's take a look at what that entails for clinical development.
· A look into compliance and privacy
When considering the usage of patient data, companies must view privacy and compliance adherence. The stakes are enormous for any technology used in clinical trial execution.
Good Clinical Practice (GCP) and validation procedures must get followed to guarantee that an outcome is legitimate by being predictable and reproducible. Furthermore, there must be openness and explainability surrounding how any AI system makes judgments to demonstrate accuracy and eliminate potential bias. This is more important than ever from a compliance standpoint since regulators consider algorithms as part of what they base their approvals on.
· Keeping the h(uman) in healthcare
Adopting AI/ML in clinical research boosts productivity via high-efficiency human augmentation and the automation of boring work rather than replacing humans with digital tools. Prior to the deployment of modern technology to clinical trials, there was an unmet demand for an agile approach that allowed researchers and organizers to focus primarily on important needs and results delivery.
The intelligent use of technology allows human engagement with AI models to improve research findings, and data science technology, even at its most sophisticated state, never replaces the human data scientist. However, it creates a mutually advantageous situation in which the augmentation of processes helps data scientists reduce data load while AI models thrive through human feedback. Continuous Integration/Continuous Delivery (CI/CD) refers to an AI model's continuous learning.
· Envisioning a bold future
AI/ML strategies are altering the clinical development cycle like never before. As the industry pushes forward, digital transformation is paving the way for extraordinary breakthroughs that permanently change the space. Today's leaders have the potential to use new technology to address traditionally difficult challenges in the field.