JMIR AI

The digitization of healthcare, facilitated by the adoption of electronic health records (EHRs) systems, has revolutionized data-driven medical research and patient care. While this digital transformation offers substantial benefits in healthcare efficiency and accessibility, it concurrently raises significant concerns over privacy and data security. Initially, the journey towards protecting patient data de-identification saw the transition from rule-based systems to more mixed approaches including machine learning for de-identifying patient data. Subsequently, the emergence of Large Language Models (LLMs) has represented a further opportunity in this domain, offering unparalleled potential for enhancing the accuracy of context-sensitive de-identification. However, despite LLMs offering significant potential, the deployment of the most advanced models in hospital environments is frequently hindered by data security issues and the extensive hardware resources required.

A major challenge in using electronic health records (EHR) is the inconsistency of patient follow-up, resulting in right-censored outcomes. This becomes particularly problematic in long-horizon event predictions, such as autism and attention-deficit/hyperactivity disorder (ADHD) diagnoses, where a significant number of patients are lost to follow-up before the outcome can be observed. Consequently, fully supervised methods like binary classification (BC), which are trained to predict observed diagnoses, are substantially affected by the probability of sufficient follow-up, leading to biased results.

Large language models (LLMs) have demonstrated powerful capabilities in natural language tasks and are increasingly being integrated into health care for tasks like disease risk assessment. Traditional machine learning methods rely on structured data and coding, limiting their flexibility in dynamic clinical environments. This study presents a novel approach to disease risk assessment using generative LLMs through conversational artificial intelligence (AI), eliminating the need for programming.

People with schizophrenia often present with cognitive impairments that may hinder their ability to learn about their condition. Education platforms powered by large language models (LLMs) have the potential to improve the accessibility of mental health information. However, the black-box nature of LLMs raises ethical and safety concerns regarding the controllability of chatbots. In particular, prompt-engineered chatbots may drift from their intended role as the conversation progresses and become more prone to hallucinations.

The application of machine learning methods to data generated by ubiquitous devices like smartphones presents an opportunity to enhance the quality of health care and diagnostics. Smartphones are ideal for gathering data easily, providing quick feedback on diagnoses, and proposing interventions for health improvement.

Spirometry can be performed in the office setting or even remotely from portable spirometers. Although basic spirometry can be diagnostic for obstructive lung disease, clinically pertinent information such as restriction, hyperinflation, and air-trapping require additional testing, such as body plethysmography, which is not as readily available. We hypothesize that spirometry data contains information that will allow estimation of static lung volumes in certain circumstances, leveraging machine learning techniques.

Language barriers contribute significantly to healthcare disparities in the United States, where a sizeable proportion of patients are exclusively Spanish speaking. In orthopaedic surgery, such barriers impact both patient comprehension and patient engagement with available resources. Previous studies have explored the utility of large language models (LLMs) for medical translation but have yet to robustly evaluate AI-driven translation and simplification of orthopaedic materials for Spanish speakers.

Recent advancements in Generative Adversarial Networks and large language models (LLMs) have significantly advanced the synthesis and augmentation of medical data. These and other deep learning–based methods offer promising potential for generating high-quality, realistic datasets crucial for improving machine learning applications in health care, particularly in contexts where data privacy and availability are limiting factors. However, challenges remain in accurately capturing the complex associations inherent in medical datasets.

Deep learning techniques have shown promising results in the automatic classification of respiratory sounds. However, accurately distinguishing these sounds in real-world noisy conditions poses challenges for clinical deployment. In addition, predicting signals with only background noise could undermine user trust in the system.

Pharmaceutical manufacturers address health care professionals’ information needs through scientific response documents (SRDs), offering evidence-based answers to medication and disease state questions. Medical information departments, staffed by medical experts, develop SRDs that provide concise summaries consisting of relevant background information, search strategies, clinical data, and balanced references. With an escalating demand for SRDs and the increasing complexity of therapies, medical information departments are exploring advanced technologies and artificial intelligence (AI) tools like large language models (LLMs) to streamline content development. While AI and LLMs show promise in generating draft responses, a synergistic approach combining an LLM with traditional machine learning classifiers in a series of human-supervised and -curated steps could help address limitations, including hallucinations. This will ensure accuracy, context, traceability, and accountability in the development of the concise clinical data summaries of an SRD.

Physician autonomy has been found to play a role in physician acceptance and adoption of artificial intelligence (AI) in medicine. However, there is still no consensus in the literature on how to define and assess physician autonomy. Furthermore, there is a lack of research focusing specifically on the potential effects of AI on physician autonomy.

The ever-evolving field of medicine has highlighted the potential for ChatGPT as an assistive platform. However, its use in medical board exam preparation and completion remains divided.