JMIR AI

There is considerable need to improve and increase the detection and measurement of depression. The use of speech as a digital biomarker of depression represents a considerable opportunity for transforming and accelerating depression identification and treatment; however, research to date has primarily consisted of small-sample feasibility or pilot studies incorporating highly controlled applications and settings. There has been limited examination of the technology in real-world use contexts.

The recent availability of law enforcement and coroner/medical examiner reports for nearly every violent death in the US expands the potential for natural language processing (NLP) research into violence.

Pharmacoepidemiologic studies, which promote rational drug use and improve health outcomes, often require Anatomical Therapeutic Chemical Classification System (ATC) drug classification within real-world data (RWD) sources. Existing classification tools are expensive, brittle, or have restrictive terms of service, and lack context that may inform classification itself.

Alzheimer disease and related dementias (ADRD) are complex disorders with overlapping symptoms and pathologies. Comprehensive records of symptoms in electronic health records (EHRs) are critical for not only reaching an accurate diagnosis but also supporting ongoing research studies and clinical trials. However, these symptoms are frequently obscured within unstructured clinical notes in EHRs, making manual extraction both time-consuming and labor-intensive.

Language, which is a crucial element of human communication, is influenced by the complex interplay between thoughts, emotions, and experiences. Psychiatric disorders have an impact on cognitive and emotional processes, which in turn affect the content and way individuals with these disorders communicate using language. The recent rapid advancements in large language models (LLMs) suggests that leveraging them for quantitative analysis of language usage has the potential to become a useful method for providing objective measures in diagnosing and monitoring psychiatric conditions by analyzing language patterns.

Generative artificial intelligence (AI) is showing great promise as a tool to optimize decision-making across various fields, including medicine. In anesthesiology, accurately calculating maximum safe doses of local anesthetics (LAs) is crucial to prevent complications such as local anesthetic systemic toxicity (LAST). Current methods for determining LA dosage are largely based on empirical guidelines and clinician experience, which can result in significant variability and dosing errors. AI models may offer a solution, by processing multiple parameters simultaneously to suggest adequate LA doses.

Properly configuring modern electronic health records (EHRs) has become increasingly challenging for human operators, failing to fully meet the efficiency and cost-saving potential seen with the digitization of other sectors. The integration of artificial intelligence (AI) offers a promising solution, particularly through a comprehensive governance approach that moves beyond front-end enhancements like user- and patient-facing co-pilots. These co-pilots, although useful, are limited by the underlying EHR configuration, leading to inefficiencies and high maintenance costs. To address this, we propose the concept of an "Elastic EHR," which proactively suggests and validates optimal content and configuration changes, significantly reducing governance costs and enhancing user experience, reducing many of the common frustrations including documentation burden, alert fatigue, system responsiveness, outdated content, and unintuitive design. Our five-tiered model details a structured approach to AI integration within EHRs. Tier I focuses on autonomous database reconfiguration, akin to Oracle Autonomous Database functionalities, to ensure continuous system improvements without direct edits to the production environment. Tier II empowers EHR clients to shape system performance according to predefined strategies and standards, ensuring coordinated and efficient EHR solution builds. Tier III optimizes EHR choice architecture by analyzing user behaviors and suggesting content and configuration changes that minimize clicks and keystrokes, thereby enhancing workflow efficiency. Tier IV maintains the currency of EHR clinical content and decision support by linking content and configuration to updated guidelines and literature, ensuring the EHR remains evidence-based and compliant with evolving standards. Finally, Tier V incorporates context-dependent AI co-pilots to enhance care efficiency, quality, and user experience. Despite the potential benefits, major limitations exist. The market dominance of a few major EHR vendors—Epic Systems, Oracle Health, and MEDITECH—poses a challenge as any enhancements require their cooperation and financial motivation. Furthermore, the diverse and complex nature of healthcare environments demands a flexible yet robust AI system that can adapt to various institutional needs that has not yet been developed, researched, or tested. The Elastic EHR model proposes a five-tiered framework for optimizing EHR systems and user experience with AI. By overcoming the identified limitations through vendor-led, collaborative efforts, AI-enabled EHRs could improve the efficiency, quality, and user experience of healthcare delivery, fully delivering on the promises of digitization within healthcare.

Artificial intelligence (AI) has advanced significantly in various fields, including medicine, where tools like ChatGPT (GPT) have demonstrated remarkable capabilities in interpreting and synthesizing complex medical data. Since its launch in 2019, GPT has evolved, with version 4.0 offering enhanced processing power, image interpretation, and more accurate responses. In medicine, GPT has been used for diagnosis, research, and education, achieving significant milestones like passing the USMLE board exam. Recent studies show that GPT 4.0 outperforms its earlier versions and medical students on medical exams.

With the explosion of innovation driven by generative and traditional AI, comes the necessity to understand and regulate products that often defy current regulatory classification. Tradition, and lack of regulatory expediency, imposes the notion of force fitting novel innovations into pre-existing product classifications or into the essentially unregulated domains of wellness and/or consumer electronics. Further, regulatory requirements, levels of risk tolerance, and capabilities vary greatly across the spectrum of technology innovators. For example, currently unregulated information and consumer electronic suppliers set their own editorial and communication standards without extensive federal regulation. However, industries like biopharma companies are held to a higher standard in the same space given current direct to consumer regulations that govern the interactions between biopharmaceutical companies, healthcare providers and patients, like the Sunshine Act (also known as Open Payments), the federal Anti-Kickback Statute (AKS), the federal False Claims Act (FCA) and others. Clear and well-defined regulations not only reduce ambiguity but facilitate scale, showcasing the importance of regulatory clarity in fostering innovation and growth. To avoid highly regulated industries like healthcare and biopharma from being discouraged from developing AI to improve patient care, there is a need for a specialized framework to establish regulatory evidence for AI-based medical solutions. In this paper, we review the current regulatory environment considering current innovations but also pre-existing legal and regulatory responsibilities of the biopharma industry and propose a novel, hybridized approach for the assessment of novel AI-based patient solutions. Further, we will elaborate the proposed concepts via case studies. This paper explores the challenges posed by the current regulatory environment, emphasizing the need for a specialized framework for AI medical devices. By reviewing existing regulations and proposing a hybridized approach, we aim to ensure that the potential of AI in biopharmaceutical innovation is not hindered by uneven regulatory landscapes.

The introduction of artificial intelligence (AI) into health care has sparked discussions about its potential impact. Patients, as key stakeholders, will be at the forefront of interacting with and being impacted by AI. Given the ethical importance of patient-centered health care, patients must navigate how they engage with AI. However, integrating AI into clinical practice brings potential challenges, particularly in shared decision-making and ensuring patients remain active participants in their care. Whether AI-supported interventions empower or undermine patient participation depends largely on how these technologies are envisioned and integrated into practice.

Insufficient participant enrollment is a major factor responsible for clinical trial failure.

Asthma-related symptoms are significant predictors of asthma exacerbation. Most of these symptoms are documented in clinical notes in free text format, and effective methods for capturing asthma-related symptoms from unstructured data are lacking.