The patient, as the person who experiences the disease first-hand, has the most direct and accurate experience of the pain of the disease and the most accurate need for health products. Although there is a vast array of technological means to combat disease and maintain health, the human burden of disease has not been reduced and the health needs of patients have not been fully met. Therefore, "patient-focused drug development" is imperative. Gathering comprehensive information from patients through multiple channels and incorporating this information into the entire drug development process can help ensure that patients’ experiences, perspectives, needs and priorities are taken into account and valued. This article will introduce the concept, development process and the specific problems it faces in patient-focused drug development.
As subjects in drug clinical trials and participants in medical practice, patients can best understand their own conditions and needs. With this in mind, the FDA proposed "patient-centered drug discovery" and issued a set of guidelines to incorporate patient experiences, perspectives, needs, and preferences into the drug development and evaluation process. Guideline (2), methods for identifying important patient information, mainly describes methods and precautions for collecting and extracting patient experience data. This paper will focus on the characteristics, common methods and precautions of qualitative, quantitative and mixed research methods in the collection of patient experience data, in order to provide help for the comprehensive collection of patient experience data.
Virtual clinical trials are clinical trials conducted through computer simulation technology, which breaks through the limitations of traditional clinical trials and has the advantages of saving time, reducing costs, and reducing the risk of human trials. With the application of new computer technologies such as population pharmacokinetics, physiologically-based pharmacokinetics, quantitative systems pharmacology, and artificial intelligence, the field of virtual clinical trials in healthcare has become an important development direction. This article will give a preliminary review of the connotation, methods and future development trends of virtual clinical trials, aiming to provide reference for the application of new technologies and methods in clinical trials.
As one of the hot topics in the field of artificial intelligence, large language models are being applied in various domains, including medical research. ChatGPT (Chat Generative Pre-trained Transformer), as one of the most representative and leading large language models, has gained popularity among researchers due to its logical coherence and natural language generation capabilities. This article reviews the applications and limitations of ChatGPT in three key areas of medical research: scientific writing, data analysis, and drug development. Furthermore, it explores future development trends and provides recommendations for improvement, offering a reference for the application of ChatGPT in medical research.
