Unstructured Electronic Health Record (EHR) data often contains critical information complementary to imaging data that would inform radiologists' diagnoses. However, time constraints and the large volume of notes frequently associated with individual patients renders manual perusal of such data to identify relevant evidence infeasible in practice. Modern Large Language Models (LLMs) provide a flexible means of interacting with unstructured EHR data, and may provide a mechanism to efficiently retrieve and summarize unstructured evidence relevant to a given query. In this work, we propose and evaluate an LLM (Flan-T5 XXL) for this purpose. Specifically, in a zero-shot setting we task the LLM to infer whether a patient has or is at risk of a particular condition; if so, we prompt the model to summarize the supporting evidence. Enlisting radiologists for manual evaluation, we find that this LLM-based approach provides outputs consistently preferred to a standard information retrieval baseline, but we also highlight the key outstanding challenge: LLMs are prone to hallucinating evidence. However, we provide results indicating that model confidence in outputs might indicate when LLMs are hallucinating, potentially providing a means to address this.

We are not aware of any concrete examples of AI already being used in farming practice, but the introduction of artificial intelligence provides the power to process huge amounts of data, pooling, and exchanging information with multiple data sources. It also provides decision support systems for complex choices that farmers and their cooperatives need to make. This gives farmers and their cooperatives a powerful tool to yield significant gains in terms of efficiency and productivity. It will be key to handle essential repetitive and diverse agricultural tasks such as weeding, harvesting crops, or milking cows. The same goes for the processing facilities for packaging logistics handled by our cooperatives. Artificial Intelligence also has a positive impact on working conditions, as it helps optimise the labour process and helps in accompanying farmers which can be significant for our sector. The same thing goes for farm and enterprises safety. We see that artificial intelligence can also support us in overcoming these huge problems. Additionally, AI can support us in tackling environmental and climatic challenges, especially in reducing the impact on the environment, reducing our carbon-footprint, and improving the functioning of the value chain. Agri-food cooperatives increasingly face the challenge of sustainable production. We are investing to improve the scope of innovations, preserve the integrity of the ecosystem, and improve the use of natural resources.

Since the 1950s, science fiction has been telling the world we will soon be living with robots. While robots have emerged, they have been mostly kept to heavy industry, where machines can perform dangerous, hot and unpleasant repetitive tasks to a high standard. But China is pioneering the move to mainstream robots in more public spheres. And the country is promising big changes in the coming decade. Robots, strange as it may seem, can play a key role in development and fighting poverty.