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The FDA establishes Big Data, Clinical Trials, and Artificial Intelligence goals
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- Understanding post-market surveillance requirements for medical devices (US & EU markets)
- Guidance for post-market surveillance and market surveillance of medical devices, including in vitro diagnostics
- Biomarkers as drug development tools: discovery, validation, qualification and use
- Clinical Biomarkers in Drug Discovery and Development
- Non-Inferiority Clinical Trials to Establish Effectiveness
- FDA Approval Process
- Three tools that help medical device SMBs work remotely
- The FDA establishes Big Data, Clinical Trials, and Artificial Intelligence goals
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The FDA establishes Big Data, Clinical Trials, and Artificial Intelligence goals
[/vc_column_text][vc_column_text]The FDA is looking to cost-effective techniques and big data to improve clinical trial efficiency, medical product development, and artificial intelligence advances. These emerging technologies have the potential to change the way they exist. However, put pressure on the US Food and Drug Administration (FDA) to update its method to reviewing new products.
- The FDA is working to improve innovation in areas with no regulatory requirements, like artificial intelligence (AI). “Artificial intelligence offers excellent potential for the future of medicine. They are actively creating a new regulatory framework to encourage innovation and AI-based technology in this sector. The FDA is looking at how it might measure the efficacy of AI technologies in the field of radio genomics, where algorithms may learn to correlate characteristics on an MRI or PET scan with tumour genomic traits to encourage AI to use.
- To evaluate the performance of AI systems, the FDA is collecting massive annotated imaging datasets. This would allow for a transparent benchmarking mechanism, allowing providers and payers to compare an AI system’s performance to that of a human physician. The FDA is increasingly turning to patient-reported outcomes and wearable device data to give insight into how patients will respond to AI in controlled clinical and real-world settings to enhance AI’s role in healthcare.
- Artificial intelligence (AI) researchers in the life sciences are under more pressure than ever to innovate rapidly. Large, multilayered, and integrated data sets provide the possibility of revealing new information and speeding up innovations. Even while there is more data than ever before, only a tiny portion of it is curated, integrated, comprehended, and evaluated. Artificial intelligence (AI) is concerned with how computers learn from data and imitate human cognitive processes.
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References
Noorbakhsh-Sabet, Nariman, et al. “Artificial intelligence transforms the future of health care.” The American journal of medicine 132.7 (2019): 795-801.[/vc_column_text][/vc_column][/vc_row]
