Artificial intelligence systems may be able to imitate human emotional responses in controlled settings, potentially opening new directions for mental health research, according to a recent study from Dresden University of Technology in Germany.

The findings come at a time when mental health conditions are rising globally, with projections suggesting that up to 1.2 billion people could be affected by 2050. Researchers say this growing challenge highlights the need for improved understanding of psychological disorders and more effective treatment approaches, particularly in talk-based therapies that are difficult to model through traditional methods.

Unlike drug development, which can rely on biological testing, psychotherapy research faces limitations because neither animal models nor human trials can fully capture the complexity of emotional and cognitive processes. Scientists involved in the study argue that large language models (LLMs) may help bridge part of this gap.

“Our results show that large language models can reproduce patterns of human affective and cognitive processes under controlled conditions,” said Dr Magdalena Wekenborg, who leads the PsychoDigital Research group at TU Dresden. She added that such systems could support efforts to better understand underlying psychological mechanisms and help explore new forms of psychotherapy research.

The study examined whether LLMs could replicate emotional states such as fear, anxiety, anger, sadness, disgust, worry, and stress when prompted. Researchers then tested whether those induced states could be altered using different emotional regulation techniques, and whether emotional prompting would lead the models to make errors similar to those seen in humans experiencing the same feelings.

Findings showed that while artificial intelligence systems do not experience emotions in a human sense, they are capable of reproducing certain patterns of emotional reasoning through language processing. This allows researchers to observe behaviour that resembles human cognitive responses under structured conditions.

The team also noted that such models offer a level of experimental control that is not possible in human or animal studies. According to researcher Jakob N. Kather, experiments can be repeated under identical conditions and adjusted systematically, allowing for more precise comparisons.

He said this could enable new data-driven approaches in psychological and biomedical research, particularly in areas where ethical or practical constraints have limited traditional experimentation.

While the study does not suggest that artificial intelligence understands emotion as humans do, it highlights how language models may serve as useful tools for exploring aspects of mental health and human cognition in ways that were previously out of reach.

Healthcare professionals are gaining significant time savings through artificial intelligence tools, but health systems are struggling to adapt quickly enough to the rapid pace of change, according to a new global report.

The findings come from the Future Health Index 2026 report published by health technology company Philips, which examined how AI is being used across hospitals and clinics and its impact on clinical workflows.

The study surveyed more than 2,000 clinicians and over 20,000 patients across 10 countries, including the United Kingdom, United States, Germany, France, China and India. It found that AI adoption among healthcare workers has increased significantly over the past year, with growing confidence in its ability to improve patient care.

More than 80% of healthcare professionals said they are optimistic about AI’s impact on patient outcomes, while around 70% believe the benefits already outweigh the risks. Many clinicians reported that AI is already making a measurable difference in their daily work.

According to the report, 46% of clinicians said they save at least 132 hours per year through AI-enabled tools, equivalent to more than three working weeks. Nurses were among those reporting the greatest time savings, particularly from reduced administrative workloads.

Shez Partovi, Chief Innovation Officer at Philips, said clinicians are increasingly able to redirect that time toward patient care, collaboration and reflection on complex medical cases. He noted improvements in work-life balance, reduced stress and greater efficiency across clinical teams.

Around 71% of respondents said AI has improved workflow efficiency, while half said it has allowed them to see more patients. Approximately the same proportion reported better work-life balance and lower stress levels.

Common uses of AI include transcribing medical notes, scheduling appointments and assisting with diagnostic tasks such as reviewing X-rays. Some clinicians also described using AI systems as support tools for clinical discussions and decision-making, including identifying potential drug interactions.

The report also found that 39% of clinicians had seen AI help identify or prevent potential medical errors multiple times in recent months, while more than 65% said it had improved their confidence in clinical decisions.

Despite these gains, the report highlights growing pressure on health systems to keep pace with demand for AI tools. Nearly two-thirds of clinicians said they turn to personal AI applications when workplace systems are insufficient, raising concerns about governance and data security.

Seven in 10 respondents said training for AI tools is limited or inconsistent, suggesting organisations are struggling to implement structured adoption programmes. Partovi said this reflects a gap between rapid technological advancement and slower institutional rollout.

He added that hospitals face complex challenges including privacy, safety, regulatory oversight and role-specific training, all of which must be addressed to ensure safe deployment.

Looking ahead, 96% of healthcare professionals expect AI to change their roles, with more than half anticipating major shifts in how they work. However, concerns remain, with 44% worried about losing clinical skills due to over-reliance on AI and 37% saying changes are happening faster than they are comfortable with.

Even so, most clinicians emphasised that human oversight remains essential. Around 86% said AI outputs must always be reviewed by healthcare professionals, while more than 80% said technology will not replace the patient-clinician relationship.