I am an oncologist. I have watched large language models (LLMs) do something I have been trying to do for years: make cancer comprehensible.A patient sits across from me with newly diagnosed cancer. I used to explain the PDL1 status and the role of neoadjuvant therapy, where patients get immunotherapy first followed by surgery — terms that meant nothing. Their eyes would often glaze but they would nod and leave confused, forced to google terms that would lead them to worst-case scenarios or fraudulent clinics.Now, they come in having asked ChatGPT or Claude to explain their pathology report. “So PDL1-positive means my cancer may respond well to specific treatment and have higher cure rates?” they ask. Overnight, we are having an intelligent conversation instead of me trying to translate oncology into English.That is extraordinary and has been genuinely useful for cancer care.The real excitementWhat thrills me most is clinical trial access. I have patients who understand why a phase II trial for immunotherapy might be appropriate for their metastatic melanoma. They have read ChatGPT’s explanation of what a trial protocol means, what endpoints are being measured, what side effects to watch for. Informed consent is now informed in a way that would not have been remotely possible five years ago.A man with advanced salivary gland cancer asked me about the open clinical trials that I hadn’t even mentioned yet. He had asked ChatGPT about maintenance therapy options and the algorithm had directed him to trial data. He understood why novel therapies made sense in his situation. He wasn’t just following my recommendation; he understood the science behind it. This represents patients becoming medically literate enough to participate in their own care.The guidelines themselves are now being simplified by LLMs into language that does not require an oncology degree to understand. A patient with cancer can ask ChatGPT to explain the staging system, treatment options, and surveillance protocols. They arrive at appointments educated and aware.This democratisation of cancer knowledge is important, especially in India, where accessing oncologists is itself challenging for many. A patient in a tier-2 city can understand what treatment options exist even before traveling to see a specialist.Known and unknown unknownsHowever, just removing the information asymmetry does not change the fact that information is not judgment. And judgment is what is hardest for us to convey.A patient comes in with stage II oral cancer. ChatGPT has explained that stage II is “intermediate risk”. My recommendation is surgery followed by radiotherapy. The patient asks: “But why can’t I just do surgery and skip radiotherapy? ChatGPT said radiotherapy has significant side effects.” The LLM gave her valid information and even explained that some stage II oral cancers don’t need radiotherapy. But it couldn’t tell her that her specific tumour with its specific characteristics had a 30% recurrence risk without radiotherapy and 10% with it. That difference is life-years. Making that judgement requires knowing her values, her tolerance for toxicity, and her specific cancer biology. An LLM cannot do that.According to one JAMA study on diagnostic accuracy, when LLMs encounter complex cases requiring contextual reasoning, their accuracy drops significantly, well below the level of an experienced physician.