But our brains aren't confined to single topics; we juggle multiple neural networks, shifting between tasks with ease. This complexity is the frontier of AI, known as Artificial General Intelligence (AGI), where machines grasp any intellectual task that a human being can. And with ChatGPT, we inch closer to this reality with a more generalized form of AI. As we strive to approach this frontier, let's explore the promising possibilities that lie ahead for med-tech in connection with AI.
AI in MedTech: current and short-term innovations
Current generation AI models — specialized models — can be seen in initiatives like the Machine-Learned Ventilator Controller research, collaboratively developed by Google AI Princeton lab and Princeton University. This research resulted in an AI-learned controller that performs, in a controlled environment, better than current methods. There are plenty of startups currently tackling the medical industry problems with single-threaded AI models, but the soaring of generic AI models will have a greater impact in the near future.
Post-pandemic financial stress intensifies the need for optimisation and innovation. Gartner's 2023 CIO Agenda Insights for Healthcare Providers reveals that AI and Machine Learning are set to be the leading technologies adopted by 2025, followed by Distributed Cloud.
The Immediate Landscape: Real-Time Predictions, Sustainability, and More
The advent of large language models (LLMs) has given rise to ambient AI, a transformative tool in the medical world. Healthcare professionals are often exhausted from balancing patient care with the task of documenting medical interactions, a necessary but time-consuming responsibility.
For that particular scenario, as well as similar cases, there are tools that use LLMs, which are trained on extensive data, to listen to ambient audio, and decipher the conversation between healthcare providers and patients. These solutions then swiftly transcribe this dialogue into a structured medical document, reducing administrative load and potential transcription errors.
Coupled with ambient sensors gathering additional patient data, this could lead to comprehensive, accurate patient profiles, aiding informed medical decisions. With ambient AI, the healthcare sector can reallocate precious time toward patient care while LLMs efficiently handle the documentation.
Other currently developed areas will benefit from the LLMs boost, such as:
The Dawn of a New Learning Era: Ready for the Revolution?
Let's take a moment and imagine Albert Einstein, at the turn of the last century, explaining the intricate theories of relativity to an individual with a lower intellectual quotient. The complexity, the leaps of thought—it would have been daunting, potentially even incomprehensible.
Fast forward to the present. Picture an Artificial General Intelligence with a capacity more than 1000 times that of the human brain. In this era, we might find ourselves as students once again, exposed to novel concepts, ideas, and theories that our current level of understanding can't yet fully grasp.
Such is the potential of the AI revolution—it's inevitable and vastly promising. We're just at the beginning, with next-gen technologies being unveiled, each one bringing us closer to a future where AGI is commonplace. Yet, in this acceleration, there's the uncertainty of regulations. It's a reminder to ensure AI, in all its potential, serves humanity's best interest.
So, are you ready for this revolution? What steps are you taking to embrace the inevitable advancements of AI? As we navigate this journey, let's remember—we're just at the tip of the iceberg, and the most exciting developments are yet to come. The Einstein of our era may not be a person, but an AGI, leading us into a future where learning never stops.
