Evidence-based medicine (EBM) holds a crucial role in clinical application. Given suitable medical articles, doctors effectively reduce the incidence of misdiagnoses. Researchers find it efficient to use large language models (LLMs) techniques like RAG for EBM tasks. However, the EBM maintains stringent requirements for evidence, and RAG applications in EBM struggle to efficiently distinguish high-quality evidence. Therefore, inspired by the meta-analysis used in EBM, we provide a new method to re-rank and filter the medical evidence. This method presents multiple principles to filter the best evidence for LLMs to diagnose. We employ a combination of several EBM methods to emulate the meta-analysis, which includes reliability analysis, heterogeneity analysis, and extrapolation analysis. These processes allow the users to retrieve the best medical evidence for the LLMs. Ultimately, we evaluate these high-quality articles and show an accuracy improvement of up to 11.4% in our experiments and results. Our method successfully enables RAG to extract higher-quality and more reliable evidence from the PubMed dataset. This work can reduce the infusion of incorrect knowledge into responses and help users receive more effective replies.

The attention mechanism is a core component of the Transformer architecture. Beyond improving performance, attention has been proposed as a mechanism for explainability via attention weights, which are associated with input features (e.g., tokens in a document). In this context, larger attention weights may imply more relevant features for the model's prediction. In evidence-based medicine, such explanations could support physicians' understanding and interaction with AI systems used to categorize biomedical literature. However, there is still no consensus on whether attention weights provide helpful explanations. Moreover, little research has explored how visualizing attention affects its usefulness as an explanation aid. To bridge this gap, we conducted a user study to evaluate whether attention-based explanations support users in biomedical document classification and whether there is a preferred way to visualize them. The study involved medical experts from various disciplines who classified articles based on study design (e.g., systematic reviews, broad synthesis, randomized and non-randomized trials). Our findings show that the Transformer model (XLNet) classified documents accurately; however, the attention weights were not perceived as particularly helpful for explaining the predictions. However, this perception varied significantly depending on how attention was visualized. Contrary to Munzner's principle of visual effectiveness, which favors precise encodings like bar length, users preferred more intuitive formats, such as text brightness or background color. While our results do not confirm the overall utility of attention weights for explanation, they suggest that their perceived helpfulness is influenced by how they are visually presented.

Large language models are thought to have potential to aid in medical decision making. We investigate this here. We start with the treatment problem, the patient's core medical decision-making task, which is solved in collaboration with a healthcare provider. We discuss approaches to solving the treatment problem, including -- within evidence-based medicine -- trials and observational data. We then discuss the chat problem, and how this differs from the treatment problem -- in particular as it relates to imitation. We then discuss how a large language model might be used to solve the treatment problem and highlight some of the challenges that emerge. We finally discuss how these challenges relate to evidence-based medicine, and how this might inform next steps.

In recent years, Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios. However, despite their potential, existing works face challenges when applying LLMs to medical settings. Strategies relying on training with medical datasets are highly cost-intensive and may suffer from outdated training data. Leveraging external knowledge bases is a suitable alternative, yet it faces obstacles such as limited retrieval precision and poor effectiveness in answer extraction. These issues collectively prevent LLMs from demonstrating the expected level of proficiency in mastering medical expertise. To address these challenges, we introduce Med-R^2, a novel LLM physician framework that adheres to the Evidence-Based Medicine (EBM) process, efficiently integrating retrieval mechanisms as well as the selection and reasoning processes of evidence, thereby enhancing the problem-solving capabilities of LLMs in healthcare scenarios and fostering a trustworthy LLM physician. Our comprehensive experiments indicate that Med-R^2 achieves a 14.87\% improvement over vanilla RAG methods and even a 3.59\% enhancement compared to fine-tuning strategies, without incurring additional training costs.

The healthcare sector, particularly tertiary-care hospitals, face an ever-increasing amount of pressure due to evolving demands aided by the growing population and unforeseen pandemics. Mounting healthcare needs directly impact patients' overall experience; including prolonged waiting periods, delayed appointments, mired level of services, and hindered ability to provide proper care. With the unprecedented global health crisis we have faced in recent years, the international healthcare system has been pushed to reform and transform. In this light, artificial intelligence (AI) and emerging technology have become increasingly prevalent, propelling efforts to improve patient care, solutions, and overall healthcare outcomes. Furthermore, the wider acceptance, and even promotion of smart technology, amongst clinicians, as a tool for informed clinical decisions has helped streamline operations, improve outcomes, and improve patient and staff satisfaction.

Artificial intelligence in medicine is currently in the infancy stage of development, but in 10 to 20 years, the capability of the technology will catch up to the hype, a speaker said at Octane's virtual Ophthalmology Technology Summit. "In the future, ophthalmologists will have to learn about AI, or you'll be vulnerable to ophthalmologists who actually know AI," keynote speaker Anthony Chang, MD, MBA, MPH, MS, chief intelligence and innovation officer at Children's Hospital of Orange County, said at the meeting. The essence of AI in medicine is moving away from evidence-based medicine to achieve precision medicine and population health. A huge information and knowledge gap must be made up by intelligence-based medicine rather than evidence-based medicine, Chang said. "This is important when we think about precision medicine, when we have so many layers of information and data that need to be gathered to make the best decision for each individual patient," he said.

In the 2012 sci-fi film'Prometheus', a robot is seen performing surgeries albeit without human control. While that may be a bit far-fetched -- as reel life is -- Artificial Intelligence (AI) in healthcare is here to stay -- whether we like it or not. AI is making inroads into healthcare like never before with a promise to make healthcare faster, accessible to everyone and cut costs. Cut to India, and the health challenges are many and diverse. There is apalpable human resource shortage and often, healthcare does not reach remote areas.

The American Medical Association (AMA) recently released its first policy recommendations for augmented intelligence. It highlights some of the most serious challenges in artificial intelligence, including the need for transparency, bias avoidance, reproducibility, and privacy. Those working in medicine may find this list familiar. Medicine has long struggled with similar problems. The similarities are not a coincidence. There are deep philosophical and methodological intersections across AI and clinical medicine. Both professions recently experienced a pendulum swing in their prevailing approaches. And in the zeitgeist of big data, powerful interests in medicine and AI are presently aligned on the same side of a centuries-long ideological struggle. People are understandably excited about a digital convergence in health tech. But ideological alignment and entrenchment may reinforce these shared challenges in a perverse codependency. The philosophical intersections between AI and medicine are not well known within their respective communities, let alone across them. Yet a positive and productive collaboration may unfold. AI and medicine embody important differences that could elevate each side and catalyze innovation.

As the amount of information in the world nearly doubles each year, detecting relevant signals through the noise is becoming even more difficult. However, successfully separating the signal from the noise, especially in healthcare, can be a tremendous value creator. For decades, the industry has been chasing the goal of evidence-based medicine, where proven science and published literature guides therapies. The application of machine learning algorithms to diverse big data sets means we can deliver the evidence at the point of care. As we combine genomic data, published literature, and other clinical data to guide therapies, we can radically and dramatically transform the patient experience and clinical outcomes.