What are the challenges and limitations of AI in Reproductive Medicine?

Time of publication: 1 year ago

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What are the challenges and limitations of AI in Reproductive Medicine?

We think the biggest challenge is actually cultural, in getting scientists and data scientists to work together as equal partners. In many companies, one group sets the direction, and the other takes a back seat. 

In the perfect world, we really need to build a culture in which scientists, engineers, and data scientists work closely together to define problems, design experiments, analyze data and derive insights that will lead us to new therapeutics. 

There are other risks associated with Ai  – let’s say two broad categories – the near term risks such as privacy issues, security,  and long-term risks such as AGI (Artificial General Intelligence). Short term risks already exist without  AI – for instance there are already many complex critical systems today that enemies could hack into like for instance the electricity grid.  

But we truly believe that the benefits of AI, ML are going to outweigh these risks. Stopping progress by stopping technology is the wrong approach. If you want to ameliorate  risks, you need to be thoughtful  about how to change societal norms and how to put in appropriate safeguards. If you don’t make progress technologically, someone else will and their intent might be considerably less beneficial than yours. You need to let technology progress and then think about the mechanisms to channel it towards good rather than bad. 

The main challenge we see is that we need to start running things in different way, i.e., accept that AI can make better decisions than us. Although, it makes very different and surprising errors then we do. Such mistakes should be overseen by humans, but in general it brings “superpowers” to the playtable. No human can learn from millions of images, and AI can. In this context the quote from Admiral and American Computer Scientist Grace Hopper comes to my mind “The most damaging phrase in the language is: We’ve always done it this way”, which comes from experience that new technologies bring many advantages with them. 

We should adapt and deploy new technologies and not stay in the status quo.

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