Large Language Models (LLMs) are increasingly used in healthcare, yet ensuring their safety and trustworthiness remains a barrier to deployment. Conversational medical assistants must avoid unsafe compliance without over-refusing benign queries. We present an iterative post-deployment alignment framework that applies Kahneman-Tversky Optimization (KTO) and Direct Preference Optimization (DPO) to refine models against domain-specific safety signals. Using the CARES-18K benchmark for adversarial robustness, we evaluate four LLMs (Llama-3B/8B, Meditron-8B, Mistral-7B) across multiple cycles. Our results show up to 42% improvement in safety-related metrics for harmful query detection, alongside interesting trade-offs against erroneous refusals, thereby exposing architecture-dependent calibration biases. We also perform ablation studies to identify when self-evaluation is reliable and when external or finetuned judges are necessary to maximize performance gains. Our findings underscore the importance of adopting best practices that balance patient safety, user trust, and clinical utility in the design of conversational medical assistants.

Michael Yu and colleagues examine the challenges in developing AI tools for use at point of care The covid-19 pandemic created unprecedented challenges for both clinicians and healthcare institutions. Adapting to a rapidly emerging disease while facing staff and material shortages prompted difficult decisions on how best to allocate resources. Artificial intelligence (AI) rapidly moved to the forefront of the effort to adapt our healthcare systems to coping with covid-19. Hundreds of new models were developed, promising best solutions for all aspects of patient care from diagnostics to therapeutics and logistics. Yet only a small minority of these models were deployed, and none became widely adopted.12 We argue that the covid-19 pandemic exposed flaws in the technological, institutional, and ethical foundations upon which AI must build to considerably improve bedside care. If AI is to be part of a rapid response to future health crises, the challenges that it faced during the covid-19 pandemic must be carefully analysed and overcome. AI is a branch of computer science that uses data and algorithms to extract meaning in a way that is characteristic of intelligent beings—that is, turning data into effective decision making processes. Research applications of AI in medicine have already emerged far and wide—for example, in drug discovery and modelling of complex biological systems. By contrast, efforts to integrate AI into everyday clinical care have had minimal success, despite the comparatively simple nature of the problems: optimising patient trajectories, maximising use of existing facilities, or determining when and how to reallocate resources. We surmise that this translational gap, which was magnified by the covid-19 pandemic, is due to the nature of the underlying data, the infrastructure through which they emerge, and the human context in which they occur. By understanding the influence of these factors on the chances …

The Healthcare sector is among the largest and most critical service sectors, globally. Recent events like the Covid-19 pandemic have furthered the challenge to handle medical emergencies with contemplative capacity and infrastructure. Within the healthcare domain, healthcare equipment supply and usage have come under sharp focus during the pandemic. The sector continues to grow at a fast pace and will record a 20.1% CAGR of surge; plus, it is estimated to surpass $662 billion by 2026. Countries like the US spend a major chunk of their GDP on healthcare.

As part of its second anniversary activities, CLAIRE hosted a webinar presenting the progress and future plans of its COVID-19 taskforce. Entitled, "CLAIRE taskforce for AI and COVID-19: results and next steps", the webinar was conducted on 15 July 2020 with a focus on the three-month research outcomes in the areas of AI for bioinformatics, drug repurposing, and medical image analysis. "When the pandemic hit Europe, we immediately thought that we have to do something to support the European government and health institutions, with CLAIRE being the biggest community of AI experts in the world," said Emanuela Girardi, co-coordinator of CLAIRE COVID-19 taskforce in her introductory note during the event. Following the launch of CLAIRE's COVID-19 taskforce on 20 March 2020, more than 150 AI researchers throughout Europe collected and curated resources which aimed to leverage AI techniques in the context of COVID-19 and to support the development of new projects in several application areas. Under this taskforce, seven major groups were formed working on mobility and monitoring data analysis; bioinformatics; medical image analysis; social dynamics and networks monitoring; robotics; and scheduling & resource management.

Intel Corp. and a group of top health-care institutions are working with an emerging artificial-intelligence technique to build a system that will make it easier for radiologists to spot brain tumor boundaries. The group, which is led by the Center for Biomedical Image Computing and Analytics at the University of Pennsylvania's medical school and includes 29 other hospitals and research organizations, said they would use a method known as federated learning to develop the AI system.