Dear Editor, Since the dawn of the technological revolution, natural language processing (NLP) has been an interesting prospect for different professions. Chat GPT, a system developed by open artificial intelligence (AI), is the latest innovation that generates text in a human-like language format. It is considered to have learned the entire internet, enabling it to create responses containing almost accurate information in answer to the prompt provided by the user1. In this letter, we would like to discuss some key areas where chat GPT can play its role in medicine. Cancer is a global healthcare crisis where a vast amount of money is annually spent, search of cure and drug development. Improvements in cancer therapeutics have increased life expectancy, which has resulted in patients requiring extended support and monitoring thereby increasing the load of healthcare professionals. In this scenario, chat GPT based chatbots which are automated artificial intelligence tools that can serve as an intermediate element between doctors and patients. They can facilitate history taking, especially of sensitive and intimate details that patients might be shy of talking about. On the other hand, these chatbots could help in decreasing costs and burdens related to healthcare resources. It can also reduce the workload of doctors by reducing the number of minor consultations which can be done by the artificially intelligent chatbot and allow the resources to be directed towards more sicker patients who might need more attention and care2. The pandemic has made the world realize the importance of remote diagnostic systems, which are not only fast, reliable, and pocket friendly but have also increased access of healthcare to disadvantaged populations. NLP systems make use of linguistics analysis and deep learning to obtain vital information from a patient prompt detailing symptom, their onset, and demographic details (3). Chat GPT works via language processing can be linked to a text messaging system where the patient can provide their details and it would suggest possible diagnoses and recommend if the patient should visit a doctor or the symptoms would go away with home remedy/over-the-counter medications. This would be possible by training the algorithm on a larger set of medical data and human surveillance to improve efficacy over a period of time. This potentially would decrease the burden of primary care providers and benefit a larger population. ---Continue