← The Health AI Brief
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Following their previous thoughts on how clinicians can strategically manage AI generated data during a consultation, an important question remains of how can individuals optimise the information that they bring to the meeting in the first place? A standard healthcare appointment often lasts less than 15 minutes, creating a significant pressure to communicate complex symptoms and history with absolute precision. Large language models offer a mechanism to bridge this gap, functioning not as a replacement for clinical judgment, but as a simple, sophisticated tool for encounter preparation. Understanding how to transition from a vague chat to a structured evidence based summary changes the dynamic of a consultation. By refining the way that these tools are prompted, it's possible to arrive at a clinic with a greater level of clarity. This discussion will detail some thoughts about high value prompting, the vital privacy boundaries that must be maintained and why the most effective use of AI might be found in the questions it helps you ask rather than the answers that it provides. The intersection of large language models and patient self care is now reality. In a lot of clinical consultations, people are no longer arriving with a single printed page from a search engine. They're arriving with structured AI generated reports and specific queries derived from neural networks. The challenge for the modern clinician is to move from a position of caution to one of strategic guidance. This involves helping people use these tools to enhance health literacy while maintaining the integrity of the clinical relationship and patient safety. A primary application for these tools is preparation for the clinical encounter. Most people leave a consultation forgetting up to half of what was discussed or realising that they forgot to ask their most pressing questions. Large language models are quite effective at generating focused question lists. If a person inputs symptoms and history, large language models can suggest specific inquiries regarding diagnostic tests, treatment options and potential side effects to consider. This can shift a patient from being a passive recipient of information to an active participant in their care. The outcome can be a more efficient use of the limited time available in a standard appointment. While preparation is valuable, the source of information remains really crucial. Standard LLM outputs can sometimes be generic or based on low quality data like unverified blog posts. Advising people to prompt for evidence based sources is an important step. People should be encouraged to ask an AI to provide information based only on authoritative guidelines or recognised medical portals such as Patient UK or the NHS website in the UK or international equivalents. This constraint can significantly reduce the risk of models hallucinating or providing outdated or slightly sketchy clinical advice. The distinction between a medical fact and a personalised diagnosis is a nuance that many people may miss. Fact based queries are where these models are currently most effective. Asking for an explanation of what plasma cells do or the mechanism of a specific medication usually yields a textbook standard answer. Because the training data on these topics is consistent and vast, there are general biological principles that don't change based on the individual. This type of usage supports the clinician by building a person's foundational knowledge before the nuances of their individual specific case are discussed. However, the risk profile changes when the query shifts towards personalised diagnosis. Clinical reasoning is a very complex process of weighing what is present, what is absent. Research indicates that a major driver of inaccuracy in patient led AI queries is the omission of relevant negative findings. We've done multiple whole episodes on this in the past. In the Channel, a clinician is trained to know that the absence of a specific symptom is as diagnostic as the presence of another. People lacking this training may not include these important details in their prompts. This can lead the AI to a conclusion that might shift dramatically if a single missing variable was included. Guiding people to understand that the AI is a reasoning assistant for facts rather than a diagnostic oracle is really essential for safety. The logic of providing more detail to an AI to get better answers is generally sound. Including things like age, education level and relevant history can help models tailor their language to that which any individual person might understand. Yet this introduces significant tension with data privacy. Every piece of information entered into a standard commercial LLM is potentially used for further training. Uploading a full medical record or a scan report means handing sensitive personal health data to a commercial entity. The strategic advice here is to keep queries general. Instead of pasting a scan report, any person can ask for the definition of a specific term, such as small vessel disease on a scan. To understand the concept we without compromising their own identity or personal information. The limitations of current technology are particularly evident in medical imaging. While vision language models can see images, they can often not interpret the visual data in a way that a radiologist does. Instead, they're frequently exploiting the text found within an image or the metadata provided in the prompt. That's the information about a scan rather than information in the scan itself. So using an AI for a second opinion on an ECG or an MRI scan is currently unreliable and potentially dangerous. And again, it's uploading personal data. The technology is not yet at a stage where it can reliably interpret raw clinical imagery without significant error rates. The framing of a question also dictates the quality of the AI's response. Leading questions will more commonly produce biased outputs. If a person asks why one treatment is preferred over the other. The the AI will likely generate a list of reasons supporting that preference, even if the premise is clinically debated. A more robust approach for asking questions is to ask for a comparison of options, requesting pros and cons of each. The neutral framing allows AI to present broader views of the clinical landscape, which a person can then discuss with their doctor and verify with confirmed sources. One of the most immediate benefits of AI in the clinic is the ability to decode medical jargon. The vocabulary of medicine is a barrier to many if a person is confused by terms like grade versus stage in an oncology context, the AI can provide clear tiered explanations within seconds. This allows people to continue the conversation until they feel that they've achieved a basic level of understanding of the specific terminology. It acts as a bridge between the highly technical language of the specialist and the health literacy of the layperson. Beyond the clinic walls, AI is providing useful for long term lifestyle management. People with chronic conditions like eczema or endometriosis are using these tools to track triggers and brainstorm adjustments to their daily routines. Again, there needs to be great care in terms of uploading personal private information, but for some it helps in identifying potential allergens in skincare products by comparing ingredient lists, for example. For others it can provide a framework for communicating their health needs to employers or colleagues. Some some find it helpful for navigating the logistical and psychological challenges of living with a condition. The ultimate goal of this technological shift isn't to replace the clinical team, but to evolve the nature of the consultation. Information is power and a better informed person is generally a more engaged patient. However, this only really works if the clinician is kept in the loop. People should be encouraged to be transparent about their use of AI. AI generated insights should be considered with an open mind but a critical eye. Efficiency in these interactions is paramount. A person arriving with an AI generated dossier of 30 pages is as much a challenge as a person with no information at all. People should consider headlines, clear, concise bullet points or screenshots of the most relevant questions. This ensures that the limited time of a consultation is spent on high value clinical decision making rather than administrative review of wordy AI transcripts. So the path forward is one of partnership. Healthcare professionals provide the responsibility, the context and the personalised care that an LLM cannot. The AI provides the scale of information and the 24 hours a day, seven days a week accessibility that a human system can't. By guiding people on how to use these tools safely, focusing on facts, avoiding privacy, risks and maintaining a critical distance from automated diagnosis, we can try and create a healthcare environment that's both more informed but more resilient.