Robust generalization is essential for deploying deep learning based tumor segmentation in clinical PET-CT workflows, where anatomical sites, scanners, and patient populations vary widely. This study presents the first cross cancer evaluation of nnU-Net on PET-CT, introducing two novel, expert-annotated whole-body datasets. 279 patients with oesophageal cancer (Australian cohort) and 54 with lung cancer (Indian cohort). These cohorts complement the public AutoPET dataset and enable systematic stress-testing of cross domain performance. We trained and tested 3D nnUNet models under three paradigms. Target only (oesophageal), public only (AutoPET), and combined training. For the tested sets, the oesophageal only model achieved the best in-domain accuracy (mean DSC, 57.8) but failed on external Indian lung cohort (mean DSC less than 3.4), indicating severe overfitting. The public only model generalized more broadly (mean DSC, 63.5 on AutoPET, 51.6 on Indian lung cohort) but underperformed in oesophageal Australian cohort (mean DSC, 26.7). The combined approach provided the most balanced results (mean DSC, lung (52.9), oesophageal (40.7), AutoPET (60.9)), reducing boundary errors and improving robustness across all cohorts. These findings demonstrate that dataset diversity, particularly multi demographic, multi center and multi cancer integration, outweighs architectural novelty as the key driver of robust generalization. This work presents the demography based cross cancer deep learning segmentation evaluation and highlights dataset diversity, rather than model complexity, as the foundation for clinically robust segmentation.

In the wake of the pandemic, a growing number of businesses are speeding up plans to adopt AI and automation, according to the World Economic Forum's Future of Jobs Report 2020. As humans and technology increasingly work together, here are five examples of the range of applications for artificial intelligence and where it might do good. Overfishing can deprive millions of people of their livelihood and billions of people of the food they need. OceanMind is a UK-based organization fighting back against overfishing and illegal fishing using AI. Pulling data from a variety of sources, including on-board collision-avoidance transmitters, radar and satellite imagery, and phone signals, OceanMind can track thousands of vessels around the world.

One of the NHS's leading hospital trusts has begun using artificial intelligence to help detect cancer in the gullet, which kills 8,000 Britons a year. It is hoped the technology will increase the number of cases of cancer in the oesophagus that doctors spot. Oesophageal cancer is one of the deadliest forms of cancer. It is hard to detect, particularly in its early stages, and many people who get it die soon after their diagnosis. Fewer than one in five of those diagnosed are still alive five years later.

Deep learning techniques can be used to triage suspected cases of Barrett oesophagus, a precursor to oesophageal cancer, potentially leading to faster and earlier diagnoses, say researchers at the University of Cambridge. When researchers applied the technique to analysing samples obtained using the'pill on a string' diagnostic tool Cytosponge, they found that it was capable of reducing by half pathologists' workload while matching the accuracy of even experienced pathologists. Early detection of cancer often leads to better survival because pre-malignant lesions and early stage tumours can be more effectively treated. This is particularly important for oesophageal cancer, the sixth most common cause for cancer-related deaths. Patients usually present at an advanced stage with swallowing difficulties and weight loss.