Abstract
<title>Abstract</title> <p> <bold>Background</bold> Liver and ovarian cancers are often diagnosed at advanced stages due to the absence of, or vague, early-stage symptoms. Current diagnostic approaches have limitations in accuracy, accessibility, and scalability, highlighting the need for innovative methods to support earlier detection. This study employed horizon scanning and evidence synthesis methods to identify emerging technologies for early detection of liver and ovarian cancers. <bold>Methods</bold> Clinical trial registries and academic databases were searched for studies published from January 2022 to October 2025. Eligible technologies for early detection or triage were estimated to be within 0–7 years of potential market access and suitable for use outside secondary care settings. Identified technologies were then mapped based on indication, test type, sample type, and technology readiness level (TRL). <bold>Results</bold> Sixty-one liver cancer technologies were identified from the literature, comprising 45 biomarker tests, five artificial intelligence (AI) and digital tests, and 11 tests integrating biomarkers and multimodal approaches. For ovarian cancer, 41 technologies were identified from the literature, comprising 24 biomarkers tests, 12 AI and digital tests, and five biomarker-integrated or multimodal approaches. Most technologies were at an early stage of development (TRL < 6), with blood-based samples (blood, serum, plasma) dominating across both cancers. In addition, 13 clinical trial records were identified including 10 for liver cancer and three for ovarian cancer. <bold>Conclusions</bold> A rapidly evolving pipeline of technologies for early detection of liver and ovarian cancers is emerging. Current development trends favour multimodal approaches that combine multiple biomarkers, or multiple modalities (e.g. combination of biomarkers and AI). The predominance of blood-based testing among identified technologies highlights the potential for implementation beyond secondary-care settings. However, robust clinical validation and investment in infrastructure, standardisation, and equitable implementation will be required before widespread adoption. </p>