The rapid expansion of scientific terrain contrasts human cognitive limits; AI-driven digital space holds potential for revolutionizing research despite challenges.

Stanford's Dr. Minor envisions AI revolutionizing medicine, improving access, research precision, and healthcare efficiency. Ethical concerns must be addressed. Patients will benefit in a decade. Opportunities for young doctors are unprecedented.

The world is amidst an AI-driven industrial revolution. Whether we let it improve the health of Americans is up to how we regulate it. There is no sector where AI can drive more immediate, life-saving impact than in biotechnology and healthcare.

This article discusses the perspective of enterprise buyers in healthcare when evaluating AI products. It emphasizes the importance of understanding the problem that needs to be solved and identifying the specific champion within the organization who owns that problem. It also highlights the need to understand the buyer's roadmap, build vs. buy framework, and best alternative. The article provides insights on selling the product, including defining the ROI case and KPIs, scoping the initial engagement in the context of user workflow, and managing data requirements. It also touches on packaging and pricing strategies and the importance of defending the product through factors such as capital, people and technology, data, and go-to-market lock-in.

Science is about to become much more exciting—and that will affect us all, argues Google's former CEO.

We believe that any new technology has to be 10 times better to successfully displace the last one – marginal improvements aren’t worth the effort. Enterprise software struggled to clear that 10x bar in healthcare; AI clears it easily.

Quebec's outdated health system obstructs AI progress. Experts warn modernization is crucial for AI potential. Challenges include data access, training, and ensuring equitable AI implementation. Quebec could excel if proactive steps are taken.

The best use for generative A.I. in health care, doctors say, is to ease the heavy burden of documentation that takes them hours a day and contributes to burnout.

AI tools are becoming increasingly common in science, with more than 1,600 researchers anticipating their importance in the next decade. However, concerns about AI's impact on research include reliance on pattern recognition without understanding, bias in data, fraud, and irreproducible research. Large language models (LLMs) like ChatGPT are both impressive and concerning, with worries about misinformation, plagiarism, and mistakes in research papers. Barriers to AI adoption include lack of resources, skilled scientists, and experience. Commercial firms dominate AI resources, and collaboration with them is seen as important. Reviewing papers that use AI is a challenge, and concerns about AI's impact on society include spreading misinformation and AI-assisted surveillance.

AI is industrializing biopharma and healthcare in everything from drug design and diagnostics to healthcare delivery and back-office functions. Bio can now scale, massively.

The article discusses Enterprise Large Language Model (LLM) projects, emphasizing their varying impacts across categories like data transformations, natural language interfaces, workflow automations, copilots, and autonomous agents. It highlights the significance of workflow automations for business value, provided the right candidates are chosen based on specific criteria. The potential of chatbots and copilots is acknowledged, but autonomous agents are seen as the ultimate productivity boost, requiring substantial resources. The article concludes by hinting at a forthcoming installment addressing poor use-case choices and risks in LLM projects.

Large language models have exhibited exceptional performance on various Natural Language Processing (NLP) tasks, leveraging techniques such as the pre-training, and instruction fine-tuning. Despite these advances, their effectiveness in medical applications is limited, due to challenges such as factual inaccuracies, reasoning abilities, and lack grounding in real-world experience. In this study, we present ClinicalGPT, a language model explicitly designed and optimized for clinical scenarios. By incorporating extensive and diverse real-world data, such as medical records, domain-specific knowledge, and multi-round dialogue consultations in the training process, ClinicalGPT is better prepared to handle multiple clinical task. Furthermore, we introduce a comprehensive evaluation framework that includes medical knowledge question-answering, medical exams, patient consultations, and diagnostic analysis of medical records. Our results demonstrate that ClinicalGPT significantly outperforms other models in these tasks, highlighting the effectiveness of our approach in adapting large language models to the critical domain of healthcare.