The integration of Artificial Intelligence (AI) into clinical research has great potential to reveal patterns that are difficult for humans to detect, creating impactful connections between inputs and clinical outcomes. However, these methods often require large amounts of labeled data, which can be difficult to obtain in healthcare due to strict privacy laws and the need for experts to annotate data. This requirement creates a bottleneck when investigating unexplored clinical questions. This study explores the application of Self-Supervised Learning (SSL) as a way to obtain preliminary results from clinical studies with limited sized cohorts. To assess our approach, we focus on an underexplored clinical task: screening subjects for Paroxysmal Atrial Fibrillation (P-AF) using remote monitoring, single-lead ECG signals captured during normal sinus rhythm. We evaluate state-of-the-art SSL methods alongside supervised learning approaches, where SSL outperforms supervised learning in this task of interest. More importantly, it prevents misleading conclusions that may arise from poor performance in the latter paradigm when dealing with limited cohort settings.

Data science plays a critical role in clinical research, but it requires professionals with expertise in coding and medical data analysis. Large language models (LLMs) have shown great potential in supporting medical tasks and performing well in general coding tests. However, these tests do not assess LLMs' ability to handle data science tasks in medicine, nor do they explore their practical utility in clinical research. To address this, we developed a dataset consisting of 293 real-world data science coding tasks, based on 39 published clinical studies, covering 128 tasks in Python and 165 tasks in R. This dataset simulates realistic clinical research scenarios using patient data. Our findings reveal that cutting-edge LLMs struggle to generate perfect solutions, frequently failing to follow input instructions, understand target data, and adhere to standard analysis practices. Consequently, LLMs are not yet ready to fully automate data science tasks. We benchmarked advanced adaptation methods and found two to be particularly effective: chain-of-thought prompting, which provides a step-by-step plan for data analysis, which led to a 60% improvement in code accuracy; and self-reflection, enabling LLMs to iteratively refine their code, yielding a 38% accuracy improvement. Building on these insights, we developed a platform that integrates LLMs into the data science workflow for medical professionals. In a user study with five medical doctors, we found that while LLMs cannot fully automate coding tasks, they significantly streamline the programming process. We found that 80% of their submitted code solutions were incorporated from LLM-generated code, with up to 96% reuse in some cases. Our analysis highlights the potential of LLMs, when integrated into expert workflows, to enhance data science efficiency in clinical research.

Despite advancements in drug development strategies, 90% of clinical trials fail. This suggests overlooked aspects in target validation and drug optimization. In order to address this, we introduce HeCiX-KG, Hetionet-Clinicaltrials neXus Knowledge Graph, a novel fusion of data from ClinicalTrials.gov and Hetionet in a single knowledge graph. HeCiX-KG combines data on previously conducted clinical trials from ClinicalTrials.gov, and domain expertise on diseases and genes from Hetionet. This offers a thorough resource for clinical researchers. Further, we introduce HeCiX, a system that uses LangChain to integrate HeCiX-KG with GPT-4, and increase its usability. HeCiX shows high performance during evaluation against a range of clinically relevant issues, proving this model to be promising for enhancing the effectiveness of clinical research. Thus, this approach provides a more holistic view of clinical trials and existing biological data.

In recent years, Large Language Models (LLMs) have demonstrated an impressive ability to encode knowledge during pre-training on large text corpora. They can leverage this knowledge for downstream tasks like question answering (QA), even in complex areas involving health topics. Considering their high potential for facilitating clinical work in the future, understanding the quality of encoded medical knowledge and its recall in LLMs is an important step forward. In this study, we examine the capability of LLMs to exhibit medical knowledge recall by constructing a novel dataset derived from systematic reviews -- studies synthesizing evidence-based answers for specific medical questions. Through experiments on the new MedREQAL dataset, comprising question-answer pairs extracted from rigorous systematic reviews, we assess six LLMs, such as GPT and Mixtral, analyzing their classification and generation performance. Our experimental insights into LLM performance on the novel biomedical QA dataset reveal the still challenging nature of this task.

The adoption of artificial intelligence (AI) and machine learning (ML) has been one of the fastest growing trends across industries over the past decade. With the continuous advancements in technology, access to ever more powerful computers, increased availability of clinical and research data, and rapid development of novel algorithms that analyze and utilize that data, interest in applying AI and ML to trial design and clinical trials to improve high failure rates is increasing. Among its many potential practical applications, AI and ML can be used to minimize errors in clinical trial participant management (e.g., cohort selection, patient identification and recruiting, participant retention) and streamline data management (e.g., automate data collection, monitor data quality, analyze large data sets).1 However, realizing the potential of this technology will require overcoming a range of different issues, including problems with data quality and access, transparency of underlying development and validation processes, potential bias inherent in the source data as well as the algorithm's implementation, and the lack of definitive regulatory guidance from the relevant government agencies. Selecting and recruiting patients for clinical trials is complicated and, despite the extensive time and effort companies put into clinical trial participant management, one of the biggest factors that causes a clinical trial to fail is failure to select and recruit the most suitable subjects for a trial.2

As an undergraduate at Princeton University, Pulkit Singh loved thinking about intelligence and how humans experience the world. She dabbled in philosophy, visual arts, and computer science, each field granting her a new way to think about the mind. During a study abroad program in Edinburgh, UK, Singh took a computational cognitive science class and knew she'd found her niche. She'd been fascinated by the brain but couldn't see herself becoming a biologist in the lab. And although she loved computer algorithms, she hadn't thought about how human and machine intelligence could benefit each other.

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At the beginning of the COVID-19 pandemic, before the widespread adoption of reliable point-of-care assays to detect SARS-CoV-2, one highly active area of research involved the development of ML algorithms to estimate the probability of infection. These algorithms based their predictions on various data elements captured in electronic health records, such as chest radiographs. Despite their promising initial validation results, the success of numerous artificial neural networks trained on chest X-rays were largely not replicated when applied to different hospital settings, in part because the models failed to learn or understand the true underlying pathology of COVID-19. Instead, they exploited shortcuts or spurious associations that reflected biologically meaningless variations in image acquisition, such as laterality markers, patient positioning or differences in radiographic projection6. These ML algorithms were not explainable and, while appearing to be at the cutting edge, were inferior to traditional diagnostic techniques such as RT-PCR, obviating their usefulness.

We are entering a new era of patient treatment options thanks to cutting-edge technologies that are changing the way life science companies approach and execute pharmaceutical research. One of the more significant solutions that support the faster and more efficient development of new pharmaceutical products – such as the COVID-19 vaccine, which was developed faster than any other vaccine in history – is artificial intelligence (AI)-driven data analysis. Thanks to modern life science technology solutions that employ AI for data analysis, new treatments for various illnesses can be made safer, faster and more focused on specific conditions. It can be difficult to develop treatments for patients dealing with rare illnesses, as it is often difficult to find enough patients to conduct thorough clinical research. Further, because the condition is rare, there may be sparse literature on the illness and even fewer specialists to consult.

Advanced technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) have become a cornerstone of successful modern clinical trials, integrated into many of the technologies enabling the transformation of clinical development. The health and life sciences industry's dramatic leap forward into the digital age in recent years has been a game-changer with innovations and scientific breakthroughs that are improving patient outcomes and population health. Consequently, embracing digital transformation is no longer an option but an industry standard. Let's explore what that truly means for clinical development. Over the years, technology has equipped clinical leaders to successfully reduce costs while accelerating stages of research and development.