ARTIFICIAL INTELLIGENCE IS NOW CAPABLE OF DESIGNING MEDICAL WEBSITES
Artificial Intelligence (AI) coding can be used to improve medical websites in various ways, from custom personalised content presentations to the integration of unique medical AI features. These include medical appointment scheduling software, healthcare cost estimators, design medical website, prescribe medication, and answer questions. By developing more targeted and custom AI solutions, some artificial intelligence healthcare platforms aim to usher in the more widespread adoption of medical AI technologies, benefitting organisations, practices, clinicians, and patients alike.
AI medical websites can use AI tools to present targeted information specific to the consumer. AI medical websites use IP addresses to indicate the user and present information specific to physicians and practices local to their area. Websites can integrate logins that capture demographic data and follow usage patterns associated with demographics. In such circumstances, information about particular conditions or treatments can be presented based on demographics, past behaviour, and the history of engagement with similarly made suggestions by the medical website’s AI features. It would be similar to how Amazon makes product suggestions, or Netflix makes suggestions for watching content. Personalised content presentation with AI creates a more satisfying user experience, which encourages more extended visits and deeper engagement to support ad revenue to help direct clients to seek medical services to their needs.
Medical AI websites can also pull in custom content from the webs that match existing site content, enabling visitors to dive more deeply into a given topic and be attentive to the latest research and information about conditions and treatments. It also can transform medical AI websites into trusted information destinations, where users come first when they want the best and latest medical information. These AI features help medical websites stand out and reflect positively on the organisations’ authority and people that stand behind them. The bigger question is how medical websites can be designed using AI.
AI Tool in Designing Medical Websites
Well, coder Sharif Shameem has shown how AI could be used to describe designs that will then be built by the AI despite it not being trained to produce code. Qasim Munye, a medical student at Kings College London, showed that the program could access information to answer medical questions using AI.
GPT-3, a tool from OpenAI, can demonstrate such capabilities by training it on an archive of the internet called the Common Crawl that contains roughly one trillion words of data. The tool is short for the Generative Pre-training Transformer and is the third generation of the machine learning (ML) model having 175 billion parameters. A parameter affects the data’s prominence in the ML tool, and changing it affects the tool’s output.
Although the product is commercially available, work still needs to be done to see how the tool should be used. The achievement looks visually impressive, with some going as far as to suggest that the tool will be a threat to the industry or even that it is creating self-awareness.
“It’s impressive, but it still has severe weaknesses and sometimes makes idiotic mistakes,” says Sam Altman, CEO of OpenAI. “Though AI is going to change the world, GPT-3 is a very early glimpse, and we still have a lot to figure out.”
The OpenAI researchers acknowledge that “GPT-3 samples can lose coherence over sufficiently lose passages, contradict themselves, and occasionally contain non-sequitur sentences or paragraphs.”
ML algorithms like these do not necessarily think or even understand the language they are responding to. Such algorithms examine a massive database of structuring a sentence and recreate a response that may have the correct outcome but do not come to the conclusions as humans do.
A lot of money and engineering effort goes into building these things, states Guy Van den Broeck, an assistant professor of computer science at UCLA. “I’m sure academics and other companies will be satisfied to use these large language models in downstream tasks, but they fundamentally change progress in AI.”
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