The stories of Blockbuster, Kodak and Borders serve as reminders of what happens when companies fail to adapt to technological advancements, leading to their decline. In the healthcare sector, we're at a similar crossroads with AI.
Notable cases like the Cleveland Clinic and UPMC stand out in demonstrating the ability to implement AI. These institutions have successfully integrated predictive AI into their systems to streamline processes and enhance patient care. Another benefit of implementing AI involves using medical imaging, such as X-rays and MRIs. Generative AI models help scientists find new disease markers, leading to better treatments and shorter clinical trials. Theis a 10% higher success rate for trials, 20% lower costs and time and quicker approval by up to two years, amplifying the value of medical projects.
Moreover, the significant challenge of high costs during the initial implementation and deployment phases can be readily addressed through, for example, open-source models instead of proprietary ones. Understanding data flow costs will help as well. Conversely, using high-capacity models, such as Gemini 1.5, which supports up to 1 million tokens, can deliver results without additional costly infrastructures and solutions.
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