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The first prospective clinical trial to evaluate the safety and performance of an artificial intelligence (AI) diagnostic aid for skin cancer detection and management in the real-world clinical setting. Participants are recruited on a consecutive basis from routine attendance at melanoma and skin cancer assessment clinics, forming a representative sample of patients and lesion phenotypes from which to evaluate AI algorithm performance. AI performance will be compared with teledermatologists' assessment, as well as to face-to-face assessors of varying clinical experience (registrars and consultant dermatologists), and with histopathology results for biopsied lesions.

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed data from a cohort of 303 patients with reverse transcription-polymerase chain reaction (RT-PCR) confirmed COVID-19, during the first phase of the COVID-19 global pandemic from 14 March to 10 September 2020. Statistical methods and survival analysis, together with the development of machine learning classifiers, were carried out on these data, with the purpose of identifying hematochemical parameters that better reflect and contribute to the risk assessment.

Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutational status is necessary. Deep learning (DL) has been successfully applied to analyze hematoxylin and eosin (H and E)-stained tissue slide images.

Cavanna L, Citterio C, Biasini C, Madaro S, Bacchetta N, Lis A, Cremona G, Muroni M, Bernuzzi P, Lo Cascio G, Schiavo R, Mutti M, Tassi M, Mariano M, Trubini S, Bandieramonte G, Maestri R, Mordenti P, Marazzi E, Vallisa D.

Eur J Cancer. 2021 Nov;157:441-449. doi: 10.1016/j.ejca.2021.08.035. Epub 2021 Sep 2.
PMID: 34601285​

Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies

Even before COVID-19, the bioinformatics labs and life science industry were investing extensively in ecosystems of technological and analytical applications/appliances to store, curate, share, integrate, and analyze large amounts of data. With the pandemic coming at an accelerating pace, a series of global research actions are being implemented to strive against the virus and its effects and to create data-driven investigations to support more agile responses to future events. Innovative solutions in COVID-19 research require more efficient and effective data management strategies and practices. Cancer research is an excellent example of the adoption of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles on precision oncology and major cancer data repositories, such as the NIH Cancer Research Data Commons, are gradually adhering to these principles.