As we look back on 2025, the world experienced a year of both remarkable achievement and profound challenge in global health. Multilateralism, science and solidarity were tested as never before, underscoring a fundamental truth: International cooperation is not optional. It is essential if we are to protect and promote health for everyone, everywhere in 2026 and beyond. Perhaps the most significant milestone was the adoption by WHO Member States of the Pandemic Agreement, a landmark step towards making the world safer from future pandemics. Alongside this, amendments to the International Health Regulations came into force, including a new "pandemic emergency" alert level designed to trigger stronger global cooperation.

Mental health support in colleges is vital in educating students by offering counseling services and organizing supportive events. However, evaluating its effectiveness faces challenges like data collection difficulties and lack of standardized metrics, limiting research scope. Student feedback is crucial for evaluation but often relies on qualitative analysis without systematic investigation using advanced machine learning methods. This paper uses public Student Voice Survey data to analyze student sentiments on mental health support with large language models (LLMs). We created a sentiment analysis dataset, SMILE-College, with human-machine collaboration. The investigation of both traditional machine learning methods and state-of-the-art LLMs showed the best performance of GPT-3.5 and BERT on this new dataset. The analysis highlights challenges in accurately predicting response sentiments and offers practical insights on how LLMs can enhance mental health-related research and improve college mental health services. This data-driven approach will facilitate efficient and informed mental health support evaluation, management, and decision-making.

With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This study addresses this gap by specifically studying the economic implications of using ML in clinical psychiatry. In this paper, we evaluate the economic implications of using ML in clinical psychiatry through using three problem-oriented case studies, literature on economics, socioeconomic and medical AI, and two types of health economic evaluations. In addition, we provide details on fairness, legal, ethics and other considerations for ML in clinical psychiatry.

What if we've been overstating its potential to a frankly dangerous degree? That's the concern of leading cancer experts in the NHS, who warn that the health service is obsessing over new tech to the point that it's putting patient safety at risk. In a sharply worded warning, the cancer experts say that'novel solutions' such as new diagnostic tests have been wrongly hyped as'magic bullets' for the cancer crisis, but'none address the fundamental issues of cancer as a systems problem'. A'common fallacy' of NHS leaders is the assumption that new technologies can reverse inequalities, the authors add. The reality is that tools such as AI can create'additional barriers for those with poor digital or health literacy'.

Healthcare provider Kaiser Permanente reached a $200-million settlement in October with the state of California over long waits experienced by patients needing behavioral health services. Greg Adams, Kaiser's chair and chief executive, cited a shortage of qualified care providers as a major reason for delays in treatment. Such shortages are prevalent statewide: In one survey, only 27% of Californians said their community has enough mental health professionals to serve the needs of local residents. Among adults in the state with any psychiatric illness, 63% said they received no mental health services in the past year. Earlier this year, I found myself among the millions of Californians with mental health needs.

Robots and AI could save a million NHS working days a year and free staff to focus on care, a blueprint for the Health Service says. Technological improvements could mean that frontline workers would no longer have to waste as much time on administrative tasks, Ministers say. The blueprint claims'robotic process automation' could handle back-office tasks up to ten times faster than humans, saving 30 per cent in costs and with fewer errors. It is one of the reforms in the NHS workforce plan, which the Government hopes will save taxpayers £10 billion. The 15-year scheme sets out radical measures to plug a deficit of up to 360,000 NHS staff by creating an army of junior and apprentice roles, and speeding up clinical training.

An artificial intelligence (AI) driven assessment tool for diagnosing mental health disorders has become the first mental health chatbot to secure a Class IIa UKCA (UK Conformity Assessed) medical device certification. Using machine learning, Limbic Access is designed to support patient self-referral through digital conversations that are incorporated into the psychological therapy pathway. The chatbot can classify common mental health disorders treated by NHS Talking Therapies (IAPTs) with an accuracy of 93%. The certification comes as NHS Improving Access to Psychological Therapies (IAPT) services are experiencing significant capacity challenges in the face of record demand. In 2021-22, 1.24 million referrals accessed IAPT services, compared to 1.02 million in 2020-21, an increase of 21.5%.

Healthcare professionals have to deal with an international system with over 30,000 codes for various diseases. It is easy to make mistakes. Researchers are now developing a computer program so that practitioners can be assisted by artificial intelligence. Because in the healthcare system, a lot of manual and time-consuming work takes place to document a patient's care. After each patient contact, the practitioner must write a discharge summary and record one or more condition codes that describe the type of examination or treatment the patient has received.

"The other thing is, because we're Allianz Partners, while it's mainly travel that we do in New Zealand, Allianz Partners globally is much more of an assistance company, so there's a whole lot of things that [the group does]." He continued: "What we did was we talked to our partners about some of the opportunities that we had and things that we could do with them, and areas where we thought we could help them to increase their reach-out to their customers." Essentially, it was a case of'What other things can we do while travel is limited,' and Allianz Partners NZ found its way towards the wellness realm, with the insurance company set to launch its digital health service (DHS) on July 18. "One of the things that [DHS] does for us is it provides our customers and business partners with immediate direct access to [five] key things," said Blyth. "Of those key things, one is an AI-based (artificial intelligence) symptom checker; the second is medical advice where they can chat with doctors." Other components of the health assistant include a round-the-clock healthline, a mental wellbeing feature, and a health services directory.

The UK's National Health Service has announced that it will test delivering vital chemotherapy drugs via drone to the Isle of Wight. The body has partnered with Apian, a drone technology startup founded by former NHS doctors and former Google employees. Test flights are due to begin shortly, and it's hoped that the system will reduce journey times for the drugs, cut costs and enable cancer patients to receive treatment far more locally. The Isle of Wight is an island two miles off the south coast of England with a population just under 150,000. Due to the short shelf-life of most chemotherapy drugs, medicines are either rushed onto the island or patients take the ferry to the mainland.