Invasive fungal diseases (IFDs) cause 1.6 million deaths/year globally and account for 10% of all hospital-acquired infections. Identification of the causative agents is essential in the management of IFDs, with the ideal diagnostic method being rapid, accurate and informative whilst remaining cost-effective. If diagnosed in a timely manner, IFDs are treatable, but despite effective antifungal therapies, the death rate from IFDs can still reach ~50%, which is largely due to the fact that fungal infections are under-recognized, and their diagnosis using conventional diagnostic techniques is typically delayed by days to weeks and lacks specificity and sensitivity. The evolution of next generation sequencing has revolutionised genomic studies to generate more data at a cheaper cost. Long-read/third generation sequencing specifically offers a unique advantage over other diagnostic methods, namely the simultaneous detection of disease agents (bacteria, viruses, fungi, etc.), their interaction, their resistance profiles and their genetic relatedness directly from clinical samples in real-time. The application of long-read sequencing to clinical diagnosis has begun to be explored through both metagenomic and metabarcoding approaches. These preliminary studies highlighted the need for further improvement and optimization, especially to increase the pathogen:host DNA ratio, high-quality DNA preparations from clinical specimens and the improvement of reference datasets and bioinformatic tool. This talk will discuss the cons and pros as well as give samples for its use in the clinical diagnosis of IFDs. The implementation of long-read based metagenomics as diagnostic tool for IFDs will result in a drastic reduction in turnaround times, from days/weeks (now) to <24h, and a timely initiation of tailored antifungal treatment, which will, in turn, improve patient outcomes, reduce morbidity and mortality, and greatly reduce health care costs.