Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GPT-4 excel in medical question answering but may face challenges in the lack of interpretability when handling complex tasks in real clinical settings. We thus introduce the diagnostic reasoning dataset for clinical notes (DiReCT), aiming at evaluating the reasoning ability and interpretability of LLMs compared to human doctors. It contains 511 clinical notes, each meticulously annotated by physicians, detailing the diagnostic reasoning process from observations in a clinical note to the final diagnosis. Additionally, a diagnostic knowledge graph is provided to offer essential knowledge for reasoning, which may not be covered in the training data of existing LLMs. Evaluations of leading LLMs on DiReCT bring out a significant gap between their reasoning ability and that of human doctors, highlighting the critical need for models that can reason effectively in real-world clinical scenarios.

Models like GPT -4 excel in medical question answering but may face challenges in the lack of interpretability when handling complex tasks in real clinical settings.

We expect our doctors to be demi-gods – flawless, tireless, always right. But they are only human. Increasingly, they are stretched thin, working long hours, under immense pressure, and often with limited resources. Of course, better conditions would help, including more staff and improved systems. But even in the best-funded clinics with the most committed professionals, standards can still fall short; doctors, like the rest of us, are working with stone age minds.

During his latest trip to Washington, OpenAI's chief executive, Sam Altman, painted a sweeping vision of an AI-dominated future in which entire job categories disappear, presidents follow ChatGPT's recommendations and hostile nations wield artificial intelligence as a weapon of mass destruction, all while positioning his company as the indispensable architect of humanity's technological destiny. Speaking at the Capital Framework for Large Banks conference at the Federal Reserve board of governors, Altman told the crowd that certain job categories would be completely eliminated by AI advancement. "Some areas, again, I think just like totally, totally gone," he said, singling out customer support roles. "That's a category where I just say, you know what, when you call customer support, you're on target and AI, and that's fine." The OpenAI founder described the transformation of customer service as already complete, telling the Federal Reserve vice-chair for supervision, Michelle Bowman: "Now you call one of these things and AI answers. It can do everything that any customer support agent at that company could do. It does not make mistakes. You call once, the thing just happens, it's done."

Microsoft has revealed details of an artificial intelligence system that performs better than human doctors at complex health diagnoses, creating a "path to medical superintelligence". The company's AI unit, which is led by the British tech pioneer Mustafa Suleyman, has developed a system that imitates a panel of expert physicians tackling "diagnostically complex and intellectually demanding" cases. Microsoft said that when paired with OpenAI's advanced o3 AI model, its approach "solved" more than eight of 10 case studies specially chosen for the diagnostic challenge. When those case studies were tried on practising physicians – who had no access to colleagues, textbooks or chatbots – the accuracy rate was two out of 10. Microsoft said it was also a cheaper option than using human doctors because it was more efficient at ordering tests.

Microsoft has taken "a genuine step towards medical superintelligence," says Mustafa Suleyman, CEO of the company's artificial intelligence arm. The tech giant says its powerful new AI tool can diagnose disease four times more accurately and at significantly less cost than a panel of human physicians. The experiment tested whether the tool could correctly diagnose a patient with an ailment, mimicking work typically done by a human doctor. The Microsoft team used 304 case studies sourced from the New England Journal of Medicine to devise a test called the Sequential Diagnosis Benchmark (SDBench). A language model broke down each case into a step-by-step process that a doctor would perform in order to reach a diagnosis.

Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GPT-4 excel in medical question answering but may face challenges in the lack of interpretability when handling complex tasks in real clinical settings. We thus introduce the diagnostic reasoning dataset for clinical notes (DiReCT), aiming at evaluating the reasoning ability and interpretability of LLMs compared to human doctors. It contains 511 clinical notes, each meticulously annotated by physicians, detailing the diagnostic reasoning process from observations in a clinical note to the final diagnosis. Additionally, a diagnostic knowledge graph is provided to offer essential knowledge for reasoning, which may not be covered in the training data of existing LLMs.

To enhance therapeutic outcomes from a pharmacological perspective, we propose MiranDa, designed for medication recommendation, which is the first actionable model capable of providing the estimated length of stay in hospitals (ELOS) as counterfactual outcomes that guide clinical practice and model training. In detail, MiranDa emulates the educational trajectory of doctors through two gradient-scaling phases shifted by ELOS: an Evidence-based Training Phase that utilizes supervised learning and a Therapeutic Optimization Phase grounds in reinforcement learning within the gradient space, explores optimal medications by perturbations from ELOS. Evaluation of the Medical Information Mart for Intensive Care III dataset and IV dataset, showcased the superior results of our model across five metrics, particularly in reducing the ELOS. Surprisingly, our model provides structural attributes of medication combinations proved in hyperbolic space and advocated "procedure-specific" medication combinations. These findings posit that MiranDa enhanced medication efficacy. Notably, our paradigm can be applied to nearly all medical tasks and those with information to evaluate predicted outcomes. The source code of the MiranDa model is available at https://github.com/azusakou/MiranDa.

Kurt'CyberGuy' Knutsson explains what health care pods mean for the industry. Imagine walking into a futuristic pod and getting a full-body scan, a blood test and a personalized health plan in minutes. That's about to become a reality if a company called Forward has its way. It just launched its flagship product, CarePod, which it claims is the world's first AI doctor's office. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER What are AI self-service healthcare pods?