Through a survey of 300 health care executives and a program of interviews with industry experts, startup leaders, and academic researchers, this report explores the best practices for success when implementing integrated digital solutions into health care, and how these can support decision-makers in a range of settings, including laboratories and hospitals. Health care is primed for digital adoption. The global pandemic underscored the benefits of value-based care and accelerated the adoption of digital and AI-powered technologies in health care. Overwhelmingly, 96% of the survey respondents say they are "ready and resourced" to use digital health, while one in four say they are "very ready." However, 91% of executives agree interoperability is a challenge, with a majority (59%) saying it will be "tough" to solve.

This study evaluates how well large language models (LLMs) and traditional machine translation (MT) tools translate medical consultation summaries from English into Arabic, Chinese, and Vietnamese. It assesses both patient, friendly and clinician, focused texts using standard automated metrics. Results showed that traditional MT tools generally performed better, especially for complex texts, while LLMs showed promise, particularly in Vietnamese and Chinese, when translating simpler summaries. Arabic translations improved with complexity due to the language's morphology. Overall, while LLMs offer contextual flexibility, they remain inconsistent, and current evaluation metrics fail to capture clinical relevance. The study highlights the need for domain-specific training, improved evaluation methods, and human oversight in medical translation.

The rapid advancements in large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI. This paper presents an overview of the current landscape of LLMs in healthcare, specifically focusing on their applications in analyzing and generating conversations for improved patient engagement. We showcase the power of LLMs in handling unstructured conversational data through four case studies: (1) analyzing mental health discussions on Reddit, (2) developing a personalized chatbot for cognitive engagement in seniors, (3) summarizing medical conversation datasets, and (4) designing an AI-powered patient engagement system. These case studies demonstrate how LLMs can effectively extract insights and summarizations from unstructured dialogues and engage patients in guided, goal-oriented conversations. Leveraging LLMs for conversational analysis and generation opens new doors for many patient-centered outcomes research opportunities. However, integrating LLMs into healthcare raises important ethical considerations regarding data privacy, bias, transparency, and regulatory compliance. We discuss best practices and guidelines for the responsible development and deployment of LLMs in healthcare settings. Realizing the full potential of LLMs in digital health will require close collaboration between the AI and healthcare professionals communities to address technical challenges and ensure these powerful tools' safety, efficacy, and equity.

Have you ever heard of emotion artificial intelligence (AI)? Emotion AI, or affective AI, is a field of computer science that helps machines gain an understanding of human emotions. The MIT Media Lab and Dr. Rosalind Picard are the premier innovators in this space. Through their work, they sparked the idea to help machines develop empathy. Empathy is a complex concept with a lot of strings attached to it, but on a basic level, it means having an understanding of another person's emotional states.

With increased pressure and demand on the healthcare system, AI allows clinicians to outsource some of the routine thinking so they can focus on the difficult parts of clinician work such as empathy, understanding, listening and delivering the human touch. A/Prof Clair Sullivan and A/Prof Guido Zuccon from The University of Queensland discuss how AI is being used in this field.

This prestigious event took place in Lisbon and was hosted by the Champalimaud Foundation at its world class facilities. The Champalimaud Foundation has the world's first pancreatic cancer research and treatment centre. The Botton-Champalimaud Pancreatic Cancer Centre has the first unit in the world designed and built specifically with the aim of researching and treating pancreatic cancer. This Centre is the result of a partnership between the Champalimaud Foundation and Maurício and Carlotta Botton, who contributed 50 million euros to its construction. Over the past 20 years, the number of people with pancreatic cancer has increased exponentially throughout most of the world, especially in the most industrialised countries.

In the hospital world there are several complex combinatory problems, and solving these problems is important to increase the degree of patients' satisfaction and the quality of care offered. The problems in the healthcare are complex since to solve them several constraints and different type of resources should be taken into account. Moreover, the solutions must be evaluated in a small amount of time to ensure the usability in real scenarios. We plan to propose solutions to these kind of problems both expanding already tested solutions and by modelling solutions for new problems, taking into account the literature and by using real data when available. Solving these kind of problems is important but, since the European Commission established with the General Data Protection Regulation that each person has the right to ask for explanation of the decision taken by an AI, without developing Explainability methodologies the usage of AI based solvers e.g. those based on Answer Set programming will be limited. Thus, another part of the research will be devoted to study and propose new methodologies for explaining the solutions obtained.

Merck, often working alongside leading academic groups and AI-driven start-ups, is also accelerating its use of advanced analytics and deep learning processes to discover and develop medicines. By integrating digital tools, we can extract hidden insights from massive datasets. This helps to predict potential compounds' properties better and create novel solutions. Digital tools and data such as omics are also helping to improve the forward and reverse translation methods used to develop precision medicines efficiently.

Digital health is defined as the space where digital technologies, daily life, and health care intersect. The objective of digital health is to enhance the efficiency of healthcare delivery and make medicine more individualised and precise. Information and communication technologies are used to help address the health problems faced by people undergoing treatment. The interconnectedness of health systems development, improved use of computational analysis, smart devices, and communication media are the techniques and tools used in digital health to aid clinicians and their clients with managing illnesses and health conditions while simultaneously promoting good health and well being. Generally, digital health is concerned about the development of interconnected health systems to improve the use of computational technologies, smart devices, computational analysis techniques, and communication media to aid healthcare professionals and their clients manage illnesses and health risks, as well as promote health and wellbeing.

A smart stethoscope combined with an AI algorithm could enable earlier diagnosis of heart failure and improved patient outcomes, all at reduced cost. Researchers at the National Heart and Lung Institute and Imperial's Centre for Cardiac Engineering have completed the first-ever NHS study to evaluate an artificial intelligence (AI) technology for point-of-care detection of heart failure. Heart failure is a condition in which the heart cannot pump blood effectively. It carries a higher risk of death than most cancers and is increasingly common, affecting 2% of the UK population and consuming 4% of the NHS budget. Reliably diagnosing heart failure is a major challenge for GPs as the symptoms, such as breathlessness are associated with many other conditions.