Artificial intelligence is rapidly transforming healthcare, with applications ranging from diagnostic imaging to drug discovery. Machine learning algorithms trained on millions of medical records can now detect certain cancers from radiological scans with accuracy that rivals, and in some cases surpasses, that of experienced radiologists. The potential of AI in diagnostics is particularly striking in fields such as dermatology and ophthalmology. Deep learning systems have demonstrated the ability to identify diabetic retinopathy — a leading cause of blindness — from retinal photographs with sensitivity and specificity exceeding 90 percent. Similarly, AI tools can analyse skin lesion images to distinguish malignant melanomas from benign moles with a degree of accuracy that matches specialist dermatologists. Beyond diagnostics, AI is accelerating the drug discovery process. Traditional pharmaceutical research involves years of laboratory experimentation and clinical trials, often at a cost of over a billion dollars per successful drug. AI-powered platforms can rapidly screen millions of molecular compounds, predicting which are most likely to be effective against specific disease targets and dramatically reducing the time and cost of early-stage research. However, significant challenges remain. AI systems are only as reliable as the data used to train them; datasets that lack diversity can produce biased algorithms that perform poorly on underrepresented patient populations. The "black box" nature of many deep learning systems also raises concerns about interpretability — doctors and patients need to understand why a particular diagnosis or recommendation was made. Regulatory frameworks are struggling to keep pace with the speed of AI development in healthcare. Ensuring that AI medical tools are rigorously validated, transparently regulated, and equitably accessible will be crucial to realising their full potential.

Every day, humans make thousands of decisions — from trivial choices about what to eat to momentous judgements about careers, relationships, and finances. The study of how people actually make decisions, as opposed to how they theoretically should, has produced some of the most influential findings in modern psychology. The seminal work of Daniel Kahneman and Amos Tversky established that human decision-making is systematically irrational. Their Prospect Theory demonstrated that people are risk-averse when considering potential gains but risk-seeking when facing potential losses. In other words, the pain of losing £100 is felt approximately twice as intensely as the pleasure of gaining £100 — a phenomenon known as loss aversion. Anchoring is another well-documented cognitive bias. When people are exposed to an initial piece of information — even a random number — it disproportionately influences subsequent judgements. Experimental subjects asked to estimate the population of a city gave higher estimates when the number "10 million" was mentioned beforehand, regardless of its relevance. The paradox of choice, described by psychologist Barry Schwartz, suggests that having too many options can be paralysing rather than liberating. When supermarket shelves display 24 varieties of jam instead of 6, customers are less likely to make a purchase and less satisfied when they do. This has implications for everything from product design to public policy. Understanding cognitive biases is not merely an academic exercise. Behavioural economists and policymakers have developed "nudge" interventions — subtle changes to the environment in which decisions are made — to steer people towards choices that benefit their long-term interests without restricting their freedom.