The legal and ethical issues that confront society due to Artificial Intelligence (AI) include privacy and surveillance, bias or discrimination, and potentially the philosophical challenge is the role of human judgment. Concerns about newer digital technologies becoming a new source of inaccuracy and data breaches have arisen as a result of its use. Mistakes in the procedure or protocol in the field of healthcare can have devastating consequences for the patient who is the victim of the error. Because patients come into contact with physicians at moments in their lives when they are most vulnerable, it is crucial to remember this. Currently, there are no well-defined regulations in place to address the legal and ethical issues that may arise due to the use of artificial intelligence in healthcare settings. This review attempts to address these pertinent issues highlighting the need for algorithmic transparency, privacy, and protection of all the beneficiaries involved and cybersecurity of associated vulnerabilities.
Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical examinations, insurance, etc. The data collected from the Internet of Things (IoT) devices attract the attention of data scientists. Data science provides aid to process, manage, analyze, and assimilate the large quantities of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to acquire factual results. The process of data cleansing, data mining, data preparation, and data analysis used in healthcare applications is reviewed and discussed in the article. The article provides an insight into the status and prospects of big data analytics in healthcare, highlights the advantages, describes the frameworks and techniques used, briefs about the challenges faced currently, and discusses viable solutions. Data science and big data analytics can provide practical insights and aid in the decision-making of strategic decisions concerning the health system. It helps build a comprehensive view of patients, consumers, and clinicians. Data-driven decision-making opens up new possibilities to boost healthcare quality.
Objective:To compare the salivary MMP -9 concentration among subjects with oral squamous cell carcinoma (OSCC), oral potentially malignant disorders (OPMD), tobacco users, and control groups. Materials and methods: A total of 88 subjects were enrolled and divided into four study groups viz., OSCC (n=24), OPMD (n=20), tobacco habits (n=22), and healthy controls (n=22). All subjects gave unstimulated saliva samples for the evaluation MMP -9 by ELISA kit. Demographic information like age, gender, type of tobacco, and duration of the habit were recorded. Results: Subjects with OSCC and OPMD had significantly higher mean MMP-9 levels than subjects with tobacco habits and control groups (P<0.001). Also, poorly differentiated OSCC group had significantly higher mean saliva MMP-9 than moderate and well-differentiated OSCC. The optimal cut-off point was 214.37 ng/mL with a sensitivity of 100% and specificity of 59% for OSCC versus the control group. The optimal cut-off point was as 205.87 ng/mL with a sensitivity of 100% and a specificity of 54% for OPMD versus the control group. Conclusion: The data obtained from this study indicated that OSCC and OPMD had an increased level of salivary MMP-9. Salivary MMP-9 could be a useful, non-invasive adjunct technique in the diagnosis, treatment, and follow-up of OSCC and OPMD.
MDA was detectable in saliva in both diabetic and control groups. There was a positive significant correlation between salivary and serum MDA in diabetic and control subjects. Hence, salivary MDA appears to be an indicator of serum MDA concentration.
Early screening of diabetes mellitus (DM) is essential for improved prognosis and effective delay of clinical complications and has been suggested as an important strategy to lower the incidence of this disease worldwide.1 Blood testing remains the standard for screening, monitoring, and diagnosis of DM, while being invasive and painful. But these techniques are inconvenient and perturb daily life, cause anxiety, and are difficult to do in long-term diabetics due to development of finger calluses, poor peripheral circulation, risk of infection, and need for skilled manpower.Recently, many studies have focused on the development of saliva-based tests for screening and monitoring systemic diseases, including DM.2-6 Saliva testing could potentially bypass the issues associated with blood tests with some distinctive advantages which would be particularly useful in the young, in the elderly, and for large-scale screening or epidemiological interventions. However, the effectiveness of saliva-based tests is still under debate. Our study was done to compare fasting salivary glucose (FSG) levels in diabetic and nondiabetic individuals and to evaluate normal cutoff of FSG levels.A total of 60 subjects known to have DM who were on medication, 60 subjects who were freshly diagnosed DM not under medication, and 60 controls (nondiabetic individuals) were included in the study. An informed consent was obtained from each subject. Subjects with xerostomia, salivary gland disorders, oral lesions with bleeding, or any other systemic illness were excluded from the study. Fasting blood glucose (FBG) was determined in all the subjects. Unstimulated whole saliva was collected in the morning after drawing the blood and salivary glucose was estimated by glucose oxidase-peroxidase method.There was significant difference in the mean salivary glucose levels among the 3 study groups (P < .001). Post hoc analysis showed that the mean FSG was highest in diabetics not under medication (11.68 ± 1.97 mg/dl) followed by diabetics on medication (9.68 ± 2.48 mg/dl) with least being in controls (6.50 ± 0.47 mg/dl). There was significant positive strong correlation between FSG and FBG in diabetics not under medication (r = .941, P < .001), diabetics on medication (r = .981, P < .001), and controls (r = .937, P < .001). ROC curves were plotted by calculating the sensitivity and specificity of salivary glucose in predicting the diagnosis of diabetes status. The area under the curve was 0.998 and was above the reference line, which suggests that the curve predicted individuals with disease. Optimal cutoff point was considered to be 7.05, with sensitivity of 99.1 and specificity of 93.7%. No studies previously have reported optimal cutoff point for salivary glucose concentration. Hence it can be extrapolated that individuals having salivary glucose level above 7.05 mg/dl may have uncontrolled DM. Thus in the present study, glucose was detectable in saliva in both diabetic and nondiabetic individuals. There was a positive correlation between FBG and FSG.H...
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