Artificial intelligence and machine learning for clinical pharmacology.

Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.

AI chatbots not yet ready for clinical use.

As large language models (LLMs) expand and become more advanced, so do the natural language processing capabilities of conversational AI, or "chatbots". OpenAI's recent release, ChatGPT, uses a transformer-based model to enable human-like text generation and question-answering on general domain knowledge, while a healthcare-specific Large Language Model (LLM) such as GatorTron has focused on the real-world healthcare domain knowledge. As LLMs advance to achieve near human-level performances on medical question and answering benchmarks, it is probable that Conversational AI will soon be developed for use in healthcare. In this article we discuss the potential and compare the performance of two different approaches to generative pretrained transformers-ChatGPT, the most widely used general conversational LLM, and Foresight, a GPT (generative pretrained transformer) based model focused on modelling patients and disorders. The comparison is conducted on the task of forecasting relevant diagnoses based on clinical vignettes. We also discuss important considerations and limitations of transformer-based chatbots for clinical use.

Gender and ethnic differences in publication of BMJ letters to the editor: an observational study using machine learning.

OBJECTIVES: To analyse the relationship between first author's gender and ethnicity (estimated from first name and surname), and chance of publication of rapid responses in the British Medical Journal (BMJ). To analyse whether other features of the rapid response account for any gender or ethnic differences, including the presence of multiple authors, declaration of conflicts of interests, the presence of Twitter handle, word count, reading ease, spelling and grammatical mistakes, and the presence of references. DESIGN: A retrospective observational study. SETTING: Website of the BMJ (BMJ.com). PARTICIPANTS: Publicly available rapid responses submitted to BMJ.com between 1998 and 2018. MAIN OUTCOME MEASURES: Publication of a rapid response as a letter to the editor in the BMJ. RESULTS: We analysed 113 265 rapid responses, of which 8415 were published as letters to the editor (7.4%). Statistically significant univariate correlations were found between odds of publication and first author estimated gender and ethnicity, multiple authors, declaration of conflicts of interest, the presence of Twitter handle, word count, reading ease, spelling and grammatical mistakes, and the presence of references. Multivariate analysis showed that first author estimated gender and ethnicity predicted publication after taking into account the other factors. Compared to white authors, black authors were 26% less likely to be published (OR: 0.74, CI: 0.57-0.96), Asian and Pacific Islander authors were 46% less likely to be published (OR: 0.54, CI: 0.49-0.59) and Hispanic authors were 49% less likely to be published (OR: 0.51, CI: 0.41-0.64). Female authors were 10% less likely to be published (OR: 0.90, CI: 0.85-0.96) than male authors. CONCLUSION: Ethnic and gender differences in rapid response publication remained after accounting for a broad range of features, themselves all predictive of publication. This suggests that the reasons for the differences of these groups lies elsewhere.