If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning, which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video. At its simplest, biotechnology is technology based on biology -- biotechnology harnesses cellular and biomolecular processes to develop technologies and products that help improve our lives and the health of our planet. We have used the biological processes of microorganisms for more than 6,000 years to make useful food products, such as bread and cheese, and to preserve dairy products. Biotechnology can be categorized into a few types agricultural biotechnology, medical biotechnology, animal biotechnology, industrial biotechnology, and bioinformatics. Let us see how Artificial Intelligence is impacting these branches of biotechnology.
An international team of researchers led by Dr. Nischalan Pillay (UCL Cancer Institute) and Dr. Ludmil Alexandrov (University of California, San Diego) used AI to identify 21 frequent faults in the structure, order, and quantity of copies of DNA present when cancer begins and progresses. These widespread errors, known as copy number signatures, could help doctors find medicines that match the tumor's characteristics. As the Netflix algorithm suggests new videos on the basis of a person's like and dislikes, the researchers developed a similar algorithm that can filter through thousands of lines of genomic data to find common patterns in the way chromosomes organize and arrange themselves. The system may then classify the patterns that develop, assisting scientists in determining the types of cancer faults that can form. DNA alterations, such as gains and losses, frequently occur in cancer and result from a variety of interconnected events, including replication stress, mitotic mistakes, spindle multipolarity, and breakage–fusion–bridge cycles, which can cause chromosomal instability and aneuploidy.
New Delhi, May 28 (PTI) An artificial intelligence-driven smartphone app has been launched by AIIMS-Delhi along with Nurithm labs, a start-up, to address the access and accuracy problems in clinical diagnosis of dermatological diseases, including skin and oral cancers. DermaAId, the skin disease diagnostic solution, uses a machine-learning AI-driven algorithm encapsulated in a mobile app and transforms a basic smartphone with a 1 MP camera into a potent tool in skincare, Dr Somesh Gupta, a Professor in the Department of Dermatology and Venereology at AIIMS, told PTI. For general practitioners, it is a clinical decision support tool to augment their capability and understanding of skin conditions. This is particularly relevant since studies have revealed that diagnostic accuracy among general practitioners vis-à-vis dermatologists is 40 to 50 per cent, Dr Gupta pointed out. "The technology behind the app is deceptively simple. A doctor takes a photo of lesions on a patient's body and uploads them to the cloud server. Within 15-30 seconds, the app provides possible disease conditions based on machine analysis of images," he explained.
Machine learning is playing an ever-increasing role in biomedical research. Scientists at the Technical University of Munich (TUM) have now developed a new method of using molecular data to extract subtypes of illnesses. In the future, this method can help to support the study of larger patient groups. Nowadays doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes.
Nowadays doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes. In biomedicine, one often speaks of the molecular mechanisms of a disease. This refers to changes in the regulation of genes, proteins or metabolic pathways at the onset of illness. The goal of stratified medicine is to classify patients into various subtypes at the molecular level in order to provide more targeted treatments, wrties the Technical University of Munich in a press release.
Although workflows are very popular, prior to the CWL standards, all workflow systems were incompatible with each other. This means that users who do not use the CWL standards are required to express their computational workflows in a different way each time they use another workflow system, leading to local success but global unportability. The success of workflows is now their biggest drawback. Users are locked into a particular vendor, project, and often a specific hardware setup, hampering sharing and reuse. Even non-academics suffer from this situation, as the lack of standards, or their adoption, hinders effective collaboration on computational methods within and between companies.
Ailing healthcare systems around the world are under the spotlight and the need for universal access to quality healthcare has never been greater as many factors continue to put pressure on the sector. These include ageing populations, clinical workforce challenges, rising utilisation stemming from a growing burden of chronic diseases like cancer, diabetes and cardiovascular disease (CVD), and reimbursement-related challenges. Reassuringly, the pandemic has forced governments to reconsider every aspect of their healthcare systems. For example, workforce size and shape, digital infrastructure, models of care focusing on primary-care pathways and digitally enabled interventions and disease surveillance, research, supply chain speed and resilience, access to care, data use, regulation, and service integration. In the UAE, health spending grew from Dh45 billion in 2016 to Dh61.7 billion in 2020.
Nigel Hughes has a thirty-six year career spanning the NHS in the UK (16 years), NGOs and patient organisations (10 years) and within the pharmaceutical industry (18 years). He has worked clinically in HIV and viral hepatitis, liver disease, and in sales & marketing, medical affairs, market access and health economics, R&D, precision medicine, advanced diagnostics, health IT and Real World Data/Real World Medicine. His experience covers clinical, education, as an advisor, consulting, communications and lobbying over the years. He is currently the Project Lead for the IMI2 European Health Data & Evidence Network (EHDEN), and was Platform Co-Lead for the IMI1 European Medical Information Framework (EMIF), as well as consulting on numerous projects and programmes in the domain of RWD/RWE. In common with all regions and countries, our health data infrastructure is reflective of how we all organically developed systems over prior systems, apart from perhaps such countries as Estonia, who were afforded the opportunity to start afresh after the end of the Soviet Union.
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The past decade has seen an important and, for many patients, a life-changing rise in the number of innovative new drugs reaching the market to treat diseases such as multiple sclerosis, malaria, and subtypes of certain cancers (such as melanoma or leukemia). In the United States, the Food and Drug Administration approved an average of 41 new molecular entities (including biologic license applications) each year from 2011 to 2020--almost double the number in the previous decade. Despite the immense costs of such achievements, 2 2. Asher Mullard, "New drugs cost US $2.6 billion to develop," Nature Reviews Drug Discovery, December 1, 2014. A major barrier is the daunting challenge of understanding the multifactorial nature of many diseases coupled with the vast set of variables in therapy design. Very few diseases, such as cystic fibrosis, are linked to variants in single genes. Drug development therefore tends to rely on a reductionist, hypothesis-driven approach that narrows the focus to individual cell types or pathways. Focused assays often based on partial information or informed by animal models that never perfectly reflect human disease then attempt to identify single molecules that will benefit patients.