Leveraging Generative AI for Health Literacy

Despite the incredible benefits of modern communication tools, they have also fostered an era where misinformation can spread like wildfire. At the same time, access to accurate, understandable and timely health information has never been more crucial.

This has put the healthcare sector in a bind, given a shortage of healthcare professionals combined with increased demand for personalized healthcare services from an aging population. About 46% of the US population was born in 1980 or later, and the percentage of the total population aged 65 or older is projected to rise from 16% to 23% by 2060 (from 53 million to 96 million).

Naturally, the healthcare ecosystem is turning to technology to help bridge the gap between healthcare supply and demand, aided by the digitization of healthcare information. Digital transformation, increased demand and exorbitant costs for healthcare services have accelerated the adoption of AI in healthcare to improve efficiency and quality of care in a system that most would describe as troubled at best. .

The global AI in healthcare market size was valued at USD 15.4 billion in 2022 and is projected to grow at a CAGR of 37.5% from 2023 to 2030, bringing the market value to over USD 210 billion of dollars. This growth should come as no surprise, given the breadth of AI adoption across all verticals and the surge in its use in healthcare a few years ago when the market increased at an astonishing rate of 167.1% from 2019 to 2021. There is no doubt the COVID-19 pandemic is driving much of this acceleration, as it has demonstrated the potential of AI in rapid diagnosis, patient management and claims settlement.

The latest AI craze, of course, is generative AI, which is being integrated into a range of tools and services, including many customer-facing technologies, like chatbots, because of its ability to generate quick and precise answers and to engage in human relations. such as conversations with users.

Healthcare certainly seems like a logical industry for GenAI innovation, given the challenges patients face getting timely information from their providers — including everyone from medical professionals to insurers. If GenAI can complement physician interactions with automation, including patient health information analysis and patient interaction, it can provide a much-needed efficiency gain for the industry.

Tell Health believes AI is, indeed, the way forward. the creator of health-focused social media app Tell has announced the launch of a generative AI feature for doctors, integrating OpenAI’s ChatGPT into its health-focused social media app. According to Tell Health, the idea is to bridge the health literacy gap by making it easier to translate complex medical jargon into plain language to make it easier for patients to understand.

“Complex medical jargon can deter people from seeking health information from real doctors and researchers, and instead turning to influencers without a medical background,” said Tell co-founder and practicing physician Alan Gaffney, MD, Ph.D.

Tell believes its ChatGPT integration solves the problem, giving medical professionals a tool to easily translate their technical medical language into something the average patient can understand and respond to appropriately.

“As we continue to realize the potential of AI in the medical community, we must remain vigilant to curb the spread of medical misinformation,” Gaffney added.

Tell’s AI functionality could help reduce health care disparities and break down barriers to accessing health information. This, in turn, would lead to more inclusive access to essential health information and trending medical topics, in essence, to the democratization of health information.

The benefits of AI in healthcare are many. AI can be trained to analyze patient health information, allowing healthcare providers to quickly diagnose conditions and design accurate treatment plans. AI algorithms have already proven effective in detecting conditions, including COVID-19 cases that were initially misdiagnosed by human professionals.

However, AI is not without risks. As the most recent episodes of the “Chicago Med” TV series show, AI is not infallible and can only work with the information it is trained on. When asked to do something beyond the scope of his training, the results can be inaccurate or even fatal in the world of health.

There is also the issue of data privacy. AI requires large amounts of data for training and operation, raising concerns about the privacy and security of sensitive health information. Additionally, some point to the potential for bias in AI algorithms, which can inadvertently favor or discriminate against certain patient populations based on training data.

If (when) errors occur, there is also a question of accountability and transparency. When a doctor makes a mistake, the answer is usually simple. When the AI ​​makes a mistake, who is at fault? The developers, the healthcare provider or the AI ​​itself?

As AI takes on more and more responsibility in healthcare, the potential for errors that could negatively impact healthcare outcomes cannot be ignored.

These kinds of issues raise questions about whether healthcare is the right sector for AI experiments. The argument could be made that in some cases, especially when there don’t seem to be positive outcomes based on human experience, AI can provide value.

Despite these potential risks, integrating AI into healthcare processes holds immense potential to improve patient outcomes, reduce healthcare costs, and address workforce shortages. The key will be to address these challenges carefully, ensuring that the benefits of AI are realized without compromising patient safety or privacy.

The integration of GenAI by Tell seems like a reasonable way to introduce generative AI into healthcare in a way that can improve the patient experience and, importantly, should facilitate the evaluation of its effectiveness and efficiency. its precision.

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