Hype is everywhere. From technology to the latest and greatest lifestyle trends, what’s “hot” today can be “ice cold” tomorrow. That’s simply life in the hyper-accelerated world in which we live.
Generative Artificial Intelligence, or Gen AI, is no exception. Yes, a lot around Gen AI started as hype, with promises of dramatic changes in how we work, the data we work with, and who is in charge: humans or machines.
So how refreshing it truly is to ponder not the hype, but the real, revolutionary breakthroughs created by Gen AI.
Today, smart life sciences companies – hungry for data and innovations to improve patient care – are learning like never before. They’re exploring the benefits of using Gen AI, with Large Language Models at its core, to dramatically improve how work gets done and to make better decisions more quickly than ever.
At Axtria, we began advocating in 2010 for a dramatic increase in the use of data within the life sciences industry we serve. Early on, we found that data analytics (today’s AI) was only as good as the data upon which it was based. Said another way, if you had bad, unreliable, or outdated data, the decisions you made would be just as bad, unreliable, or outdated. It was simply “garbage in, garbage out,” as Axtria’s President and CEO, Jassi Chadha, stated in a recent article in BioSpectrum.
Clearly, winning the data battle is job one. That’s what makes it so refreshing to see real advancements – credible, insightful, reliable Gen AI technologies – becoming so prevalent in so many places when you get this right.
Early on, clients wanted to jump on the Gen AI bandwagon. But, as in many innovations, they didn’t know what they didn’t know. Sure, they had access to formal research and reports – the “structured” data in the world of Gen AI. But they didn’t know, at first, that the mother lode of insights comes from “unstructured” data. In our work with life-sciences clients, that could mean things such as doctor’s notes, recordings from patient examples, and unusual yet insightful data that doesn’t fit into a simple spreadsheet, all ripe for a Gen AI model’s picking.
Today, the embrace of Gen AI is as real as it gets. The hype has moved on to other areas. In fact, Gartner has issued a fascinating report about this, called Predicts 2024: Generative AI Brings New Value to Life Sciences.
In its report examining real impacts Gen AI is bringing to the industry, Gartner cited several observations of particular importance regarding the use of Gen AI in the selling process and the anticipated rise of virtual assistants:
There’s a lot we’ve learned in today’s world of Gen AI within the life sciences industry. The discussion has only just begun. From our experience in providing technology and domain expertise, we’ve developed some specific insights of our own that are already helping our life sciences clients. These include:
In the end, Gen AI platforms are only as good as the time, care, and data that power their efficacy. Bad data not only drives bad decisions, but it can also create wrong answers to critical queries asked by users. These “hallucinations” can emerge when “stale” information has grown irrelevant or incorrect. There is a solution: Deploy advanced frameworks like Retrieval-Augmented Generation, or RAG, which access external knowledge to supplement internal data. We share more on managing, and avoiding, hallucinations.
Ironically, Generative AI’s promise to drive us forward into the future – managing data, working smarter, and getting super-fast access to information – is only as good as the engineering put to work in organizing and manipulating the vast treasures of structured and unstructured data from the past.
The good news is that this work is real and no longer hype.
Gartner, Predicts 2024: Generative AI Brings New Value to Life Sciences, Reuben Harwood, Animesh Gandhi, Michael Shanler, Jeff Smith, 10 January 2024.
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