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Researchers did a study on whether AI can be a reliable source of cancer treatment recommendation. Well, let’s find out…
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The Dangers Of AI In Healthcare
In a recent article published in JAMA Oncology, researchers delved into the world of chatbots driven by LLMs powered by AI. The focus was to determine if these AI-driven chatbots could effectively offer accurate cancer treatment recommendations.
These language models are impressive in their ability to mimic human conversation patterns and provide detailed responses to queries. However, it's worth noting that there are instances where their information might be less reliable. This could pose a challenge for individuals who rely on AI for self-education. Biases, though minimized with reliable data, still plague AI systems, which raises concerns about their applicability in medical contexts.
The study highlighted a potential pitfall: users might turn to AI-powered chatbots for cancer-related advice. Yet, even if the information seems correct, there's a chance the advice could be inaccurate. This is a significant concern, as a seemingly accurate response that's off the mark in terms of cancer diagnosis or treatment could lead to misinformation being propagated.
The researchers aimed to assess an LLM chatbot's performance in providing treatment recommendations for prostate, lung, and breast cancers. To do this, they relied on the 2021 National Comprehensive Cancer Network (NCCN) guidelines, as ChatGPT’s knowledge was updated only until September 2021. They crafted various prompts to test the chatbot's accuracy against these guidelines.
The study encountered a few hurdles. Sometimes, the chatbot's responses were unclear, leading to disagreements among the researchers. The study team, including four board-certified oncologists, assessed the chatbot's output and resolved uncertainties collaboratively.
The study analyzed a total of 104 prompts with five scoring criteria, yielding 520 scores. Annotators agreed on 322 of these scores, indicating a moderate consensus. Impressively, the chatbot offered at least one recommendation for 98% of the prompts. All treatment recommendations matched NCCN guidelines, but some outputs suggested treatments that weren't in line with these guidelines.
The study brought to light the intricacies of working with LLM chatbots. Their responses could be influenced by the phrasing of questions, and differing interpretations of guidelines sometimes led to discrepancies in assessments. Notably, even experts found it challenging to identify the chatbot's mistakes.
In essence, the LLM chatbot's performance in this study was middling. It occasionally mixed accurate recommendations with incorrect ones, and even expert oncologists couldn't always spot the errors. Roughly a third of its treatment recommendations didn't align with NCCN guidelines, highlighting the chatbot's limitations.
With the growing use of AI in healthcare, the study's findings underscore the importance of patient education. People need to be aware of the potential for misinformation when relying on AI-driven technologies. Additionally, this research emphasizes the necessity for robust regulations to govern the use of AI and similar technologies, particularly in contexts where incorrect information could have serious consequences.
In a world where AI is becoming increasingly integrated into healthcare, this study serves as a reminder of the delicate balance between innovation and accuracy.
Would you trust AI in healthcare? |
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