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Explainable Artificial Intelligence: Guardian for Cancer Care
| Content Provider | Scilit |
|---|---|
| Author | Ahuja, Vishal |
| Copyright Year | 2021 |
| Description | Book Name: Explainable Artificial Intelligence for Smart Cities |
| Abstract | Artificial intelligence has contributed immensely to the development of smart cities by revolutionizing each sector of society like education, transport, trading, weather forecasting, and even healthcare. Rapidly growing population and unhealth lifestyle increased the frequency of several health ailments like diabetes, cancer, and other metabolic disorders. Cancer is characterized by irregular growth, abnormal bleeding, tissue necrosis, and death but the phenomena are much more complex at the molecular level and governed by multiple biochemical reactions, expression-suppression of many genes, and involvement of immune response cell, and vice versa. Accurate, timely diagnosis and availability of therapeutics are the essential conditions for treatment. Numerous potent bioactive molecules have been claimed for anti-cancerous potential, among which very few pass the clinical trials. On the other hand, screening of bulk molecules/formulations would prove intensive in terms of time as well as cost, and even then, there is no surety for positive results. Therefore, drug formulation needs to adopt some alternate strategies like in-silico analysis for rapid and cost-effective screening. ‘Explainable Artificial Intelligence’ (XAI) plays an inevitable role in diagnostics and drug formulation by linking the gaps between machine learning and neural network. It offers numerous tools to furnish structure prediction, interactions with target sites/receptors, and establishes the relation between structural/physiological characteristics of proteins and sequence (quantitative structure-property relationships or quantitative structure-activity relationships; QSPR/QSAR), and ADMET (absorption, distribution, metabolism, excretion, and toxicity). In comparison to wet-lab screening, in-silico tools reduced the time taken for drug formulation by at least 50%. The available results have shown a promising future for XAI in healthcare as a lot of work has to be done for human welfare. The current chapter summarizes the advancements in the field of healthcare with special concern to cancer taking into consideration ‘Explainable Artificial Intelligence’ and its role in drug design with special context to cancer. |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003172772-5&type=chapterpdf |
| Ending Page | 81 |
| Page Count | 17 |
| Starting Page | 65 |
| DOI | 10.1201/9781003172772-5 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-09-16 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Explainable Artificial Intelligence for Smart Cities Metabolic Disorders Structure Artificial Diabetes Proteins Treatment Neural Intelligence Cancer Drug Formulation |
| Content Type | Text |
| Resource Type | Chapter |
