首页|New Machine Learning Study Findings Have Been Reported by Researchers at University of Deusto (Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications)
New Machine Learning Study Findings Have Been Reported by Researchers at University of Deusto (Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications)
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Investigators discuss new findings in artificial intelligence. According to news originating from Bilbao, Spain, by NewsRx editors, the research stated, “Quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference.” Financial supporters for this research include European Union’s Horizon 2020 Research And Innovation Program Under The Marie Sklodowska-curie; Evida Research Group, University of Deusto, Bilbao, Spain, Through The Basque Government. The news reporters obtained a quote from the research from University of Deusto: “These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and full-text criteria, respectively.”
University of DeustoBilbaoSpainEuropeComputersCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
IEEE
