Powered by advances in machine- and deep-learning techniques, artificial intelligence (AI) has been hailed as the answer to a wide range of persistent healthcare challenges. But while predictive AI models have shown promise in areas such as early disease detection, infectious disease surveillance and more, there are many healthcare issues for which these techniques are ill-suited.
Generative AI, with its ability to ingest and process unstructured data and produce human-like outputs with minimal prompting, offers vast promise to improve health outcomes by engaging patients across a range of healthcare interactions. This white paper, a collaboration between World Economic Forum and ZS, examines the most promising use cases for patient-first generative AI in healthcare, the biggest barriers to adoption and the steps leaders from healthcare and beyond can take to overcome them.
- Gen AI holds significant promise to guide healthcare consumers across their full health journeys.
- Barriers include mistrust among doctors and the public, holes in the data foundation and scalability of gen AI into low-resource environments.
- To overcome these barriers, stakeholders must work to instill models with empathy and domain-specific knowledge, overcome bias through connecting the data ecosystem and continuously fine-tuning models, keeping humans in the loop and developing less expensive ways to train and run large multi-modal models.
“I truly believe that generative AI will prove to be the single most profound technological breakthrough in my lifetime,” said ZS CEO Pratap Khedkar, who co-authored the white paper. “However, it will take a concerted effort by society to put appropriate protections in place to ensure its safe and responsible use—especially in a sphere as sensitive as healthcare. We have the tools to do so, but we will have to summon the common will.”
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