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Applications of Health Data
Published in Disa Lee Choun, Anca Petre, Digital Health and Patient Data, 2023
With the use of patient data, doctors would be able to analyse earlier on any signs of conditions and provide an early diagnosis. Data analytics is key for preventive and predictive medicine. Many robust early detection services and other health-related technologies have developed from clinical and diagnostic evidence in both the data mining type of companies and healthcare providers. Machine learning and AI are commonly used in the research and healthcare sectors. These involve collecting and analyzing patient data with remote monitoring devices that includes diagnostic tools. This helps to accelerate diagnoses and disease predictions, augment doctors or researchers’ decision-making, deliver diagnostic insights to help clinicians make faster and more accurate diagnoses, and provide continuous care to patients.
Genetic testing and risk perception in the context of personalized medicine
Published in Ulrik Kihlbom, Mats G. Hansson, Silke Schicktanz, Ethical, Social and Psychological Impacts of Genomic Risk Communication, 2020
Sabine Wöhlke, Marie Falahee, Katharina Beier
Genetic information is of increasing importance in medical research and health care. For example, progress in predictive medicine enables the early detection and diagnosis of diseases. On the one hand, genetic knowledge may improve the provision of health care services, e.g. by determining the most effective therapy and avoiding ineffective or even futile treatments. Moreover, genetic information can have beneficial effects for the individual, e.g. by revealing health-relevant information for the prevention of certain diseases (Burke 2014). On the other hand, however, genetic information can also have detrimental effects. For example, knowing that one has an increased or even certain risk of developing an untreatable disease may not only be highly burdensome but can also increase the risk of psychological stress, discrimination and stigmatization (Kalokairinou et al. 2018; Ross et al. 2013; Borry et al. 2014).
Cancer Nanotechnology for Molecular Profiling and Individualized Therapy
Published in Brian Leyland-Jones, Pharmacogenetics of Breast Cancer, 2020
May Dongmei Wang, Jonathan W. Simons, Shuming Nie
Cancer markers are broadly defined as altered or mutant genes, RNA, proteins, lipids, carbohydrates, small metabolite molecules, and altered expression of those that are correlated with a biological behavior or a clinical outcome (19–22). Most cancer biomarkers are discovered by molecular profiling studies on the basis of an association or correlation between a molecular signature and cancer behavior. In the cases of both breast and prostate cancers, a major progression step is the appearance of so-called “lethal phenotypes” (causing patient death) such as bone metastatic, hormone-independent, and radiation- and chemotherapy-resistant phenotypes. It has been hypothesized that each of these aggressive behaviors or phenotypes could be understood and predicted by a defining set of biomarkers (20). By critically defining the inter-relationships between these biomarkers, it could be possible to diagnose and determine the prognosis of a cancer on the basis of a patient’s molecular profile, leading to personalized and predictive medicine.
Clinical and epidemiological observations on individual radiation sensitivity and susceptibility
Published in International Journal of Radiation Biology, 2020
Petra Seibold, Anssi Auvinen, Dietrich Averbeck, Michel Bourguignon, Jaana M. Hartikainen, Christoph Hoeschen, Olivier Laurent, Georges Noël, Laure Sabatier, Sisko Salomaa, Maria Blettner
Besides the ethical issues related to the development of this type of predictive medicine, the identification of radiosensitivity/radiosusceptibility also raises the legal issue who is responsible for the results of the assay(s) especially, if they include the exposure to ionizing radiation of a tissue sample (lymphocytes, fibroblasts…). One can imagine that clinical laboratory technologists would be authorized to practice for such assays. Additional questions regarding the legal as well as the ethical aspects arise if artificial intelligence would be used to evaluate assays or other markers, and such results should be used for personalizing diagnostic approaches and/or therapeutic applications of IR.