AI and the Science Lab of the Future
According to a Reports and Data report, the global computer vision market is forecast to reach USD 25.69 Billion by 2028. Computer vision(CV), a form of artificial intelligence that uses an algorithm to identify, process, and analyze vast amounts of information, such as texts, images and videos, is increasingly becoming a high-demand requirement to achieve digital transformation.
Recently, we published an article, Computer Vision is Providing a New Lens Into Biopharma Innovation, where I discuss computer vision’s role in biopharma.
AI in Life Sciences
Computer vision is pushing for new standards for intelligent medical devices, diagnostics and treatments that will establish new norms for the people affected by them. The future for scientific labs will entail human intelligence being amplified by AI, specifically computer vision.
Digital transformation via computer vision will become the strategy of choice in life sciences, due to the need for faster diagnoses and quicker commercialization of medical devices and pharmaceuticals. Here are applications to consider:
- Vaccine delivery. Computer vision has the ability to sort through thousands of biological images and data to identify those that are most likely to trigger an immune response. By depending on vast amounts of data, computer vision is able to detect the proteins that make up a virus, as well as those that are mutating. Additionally, it is tracing toxicity markers, disease triggers and aiding in the discovery of molecular combinations.
- Rapid at-home testing. By applying computer vision to art-home tests, it is able to identify the presence of COVID-19 and other viruses. In the near future, a mobile app will be available for a rapid antigen single-use, self-test that will provide a speedy diagnosis.
- Medical diagnostics. Computer vision models are now being used to read scans with extreme accuracy, by using X-ray and MRI images. This is excellent for medical professionals because it is helping them quickly identify the presence of diseases, recognize anomalies and assess the severity of a disease case faster.
- Clinical trials. Using computer vision for tracking and identifying vast amounts of datasets collected during clinical trial processes helps assess the efficacy of drugs and their impact on the patients participating. This would make the researcher’s process quicker.
- Assessing biomedical data. Computer vision can help analyze images and data and identify biologically relevant information and correlation, overcoming the potentially unstructured patterns of the medical data available.
The emergence of the necessity of AI in life sciences, such as computer vision, is guiding the evolution of the regulatory environment for AI-driven pharmaceutical methods and medical devices.
The constant need for training once new data arrives, means the inspection guidelines for AI are evolving. These may require new FDA approval each time the algorithm is modified in a significant way. Still, the goal continues to be that AI-assisted discoveries, diagnostics and treatments are providing safe and effective outcomes.
The Future of Computer Vision
Computer vision has the potential to improve outcomes outside of the traditional labs, clinics and hospitals. Other areas to consider include patients in war and natural disasters. Equipping hand-held MRI machines with AI algorithms could help detect conditions on the spot. This could also benefit patients that require urgent care.
Challenges to Overcome
The current cost to adopt computer vision is functioning as an obstacle. Companies depend on a transformative shift to be able to integrate computer vision. They also require investments by other companies to build a better tech infrastructure. CPU power is also needed for the processing of huge datasets. There is a lack of affordable CPU power. The cloud and AI innovation will proliferate the market, while major players, such as Apple and Intel, continue to work to deliver affordable CPU power.
Computer vision is proving to be essential for clinicians and life science professionals looking for support during trying times. It will continue to help them receive highly accurate data-driven results and help them focus on giving their best effort and delivering optimal patient care.
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