In and out of the life sciences, the conversation about artificial intelligence (AI) is impossible to avoid. Because AI has crossed over into mainstream culture, the discussion about the pros and cons of its use is amplified. As with any new technology, there is fear and calls for an immediate half-year moratorium on research.
The flip side is the radical optimism espoused by Sam Altman of Open AI, with statements about its ability to improve the human condition. This sentiment is at the centre of the concept of human-centric AI, which we at eLabNext believe can benefit the biotech community. The following blog will discuss the basics of human-centric AI and how it can drive positive change in today’s modern biotech labs.
Human-centric AI refers to designing, developing, and deploying AI systems that prioritize the well-being, needs, and values of humans. In other words, it’s the use of AI to improve the human condition.
To ensure that AI systems are developed and deployed in ways that align with human interests, some guiding principles have emerged to help those actively engaged in AI work towards improving the human condition.
AI is already being applied in healthcare, where it’s being used to directly enhance the human condition with better disease detection and prediction.
Further upstream, in the biotech discovery or drug and diagnostic development spaces, human-centric AI enables vetting drug candidates, developing fruitful pre-clinical testing strategies, and more. There have been early adopters of AI systems and those who are more cautious, waiting until the dust clears to implement AI into their workflows.
Whether you fall into one camp or another, AI implementation in a laboratory environment requires a robust digital infrastructure. For those utilizing old-school, in-house built systems or pen-and-paper record-keeping with no long-term digitalization strategy, harnessing AI’s power is bound to be a multi-stage, lengthy, and costly process. The foundation for being a human-centric AI biotech company is having a robust digital foundation across the board, from day-to-day sample management to large-scale raw file data control.
Ultimately, it comes down to having a Digital Lab Strategy that can lead your organization to implement human-centric AI more seamlessly, either now or in the not-too-distant future.
Are your samples and experiments digitized? Can you easily access and analyze your data? Is there a healthy collaborative culture and technical capability?
If so, then the rest is easy. Schedule a free personal demo with our digitalization specialist to get started!
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