Two articles in The Lancet Oncology report on the development of cancer workforce and the impact of artificial intelligence
A global crisis
A new Lancet Oncology Commission report led by Prof. Hedvig Hricak (Scientific Advisory Board of the Medical University of Vienna) warns that the global shortage of cancer-care personnel is a key driver of survival disparities between high-income countries and low- and middle-income countries (LMICs), modelling that the workforce gap could reach roughly 100 million by 2050—most acutely among nurses and diagnostic specialists in Africa and Asia. The Commission projects that comprehensively scaling up the workforce could avert 170 million cancer deaths and generate net economic benefits of around US$120 trillion between 2030 and 2050, a return of roughly $4 for every $1 invested. Among the pragmatic solutions proposed, the authors highlight task-shifting, digital health, and artificial intelligence as tools to boost workforce productivity and help close the gap.
The Commission frames artificial intelligence as a clinician-augmenting tool that could help narrow the projected global cancer workforce shortage by boosting productivity—supporting diagnostic tasks in radiology and pathology, easing documentation and administrative burden
"The shortage of well-trained personnel to deliver cancer care and conduct research remains a major obstacle to reducing disparities in cancer survival between high-income countries and low-income and middle-income countries (LMICs)."
Synergies and transformation
In a comment accompanying the Lancet Oncology Commission Prof. Helmut Prosch and Prof. Georg Langs (CAIM) argue that AI-augmented workflows and task-shifting offer a path to ease severe global radiology and diagnostic workforce shortages—especially in LMICs, where diagnostic specialists are 15 times scarcer than in high-income countries and nearly half the world lacks access to appropriate diagnosis—citing workload reductions such as 44% in breast screening (MASAI) and up to 65% in lung cancer screening. The authors contend that AI's greatest value lies not in solving isolated tasks but in fundamentally redesigning care pathways along three axes—prioritisation, revising pathways, and enablement—shifting experts toward complex cases and converging radiology, pathology, and molecular diagnosis. Crucially, they warn that AI must be co-developed with LMIC communities and steered toward broadening access to effective cancer care, rather than passively deployed in ways that merely automate fragments of already well-resourced systems.