Meça, Alba and Ali, Maaruf (2025) A comparative analysis of startups applying AI in clinical oncology. In: Proceedings of the 2024 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA ’24), Universiteti Metropolitan Tirana (UMT), Tirana, Albania, 18th -19th December, 2024, 18-19 Dec 2024, Tirana, Albania.
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Ali, Maruuf (2024) A comparative analysis of startups.pdf - Accepted Version Available under License CC-BY Creative Commons Attribution. Download (1MB) |
Abstract
The rise and exploitation of artificial intelligence (AI) in general and in particular the medical field is fuelled by breakthroughs in deep learning (DL), advances in computing hardware devices, as well as the exponential growth of clinical data used for decision-making. Modern oncological research is intensively adopting AI-based technologies, with most of the related elements such as machine learning (ML) and DL models being utilised to improve the accuracy and efficiency of cancer diagnosis, prevention and treatment, with studies showing that AI could have many additional applications in cancer care. In most countries, numerous AI technologies have received governmental approvals for use in oncology, most notably in radiology. In response to the growing interest in the use of AI technologies in clinical oncology, the number of AI start-ups that concentrate their efforts on combating cancer has surged. The present research study adopts a mixed methodological approach to perform a comparative analysis of the top five AI start-ups that are focused on the application of AI technologies in clinical oncology. The five chosen AI start-ups include: CancerIQ (USA); Panakeia (UK); MultiplAI Health (UK); MNM Bioscience (USA) and X-Zell in Singapore. The selection of the AI start-ups was based on the availability of statistical metrics of interest to the study and the impact of their projects and applications on clinical oncology. The study performs a quantitative and qualitative analysis of selected metrics associated with the use of AI in clinical oncology and the related financial performance metrics.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | AI, Artificial Intelligence, Care Pathway, Clinical Decision Support, Deep Learning, DL, Omics, Oncology, Precision Medicine, Start-ups |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Institutes and Academies > Wales Institute for Science & Art (WISA) > Academic Discipline: Engineering |
| Related URLs: | |
| Depositing User: | Muhammad Maaruf Ali |
| Date Deposited: | 04 Mar 2025 15:49 |
| Last Modified: | 05 Mar 2025 10:09 |
| URI: | https://repository.uwtsd.ac.uk/id/eprint/3476 |
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