Machine Learning In Radiation Oncology Theory And Applications Pdf
File Name: machine learning in radiation oncology theory and applications .zip
- Artificial intelligence in healthcare
- Deep learning-enabled medical computer vision
- Machine learning in radiation oncology : theory and applications
Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis.
Artificial intelligence in healthcare
Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence AI , to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data.
Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data. What distinguishes AI technology from traditional technologies in health care is the ability to gather data, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms and deep learning.
These algorithms can recognize patterns in behavior and create their own logic. To gain useful insights and predictions, machine learning models must be trained using extensive amounts of input data. AI algorithms behave differently from humans in two ways: 1 algorithms are literal: once a goal is set, the algorithm learns exclusively from the input data and can only understand what it has been programmed to do, 2 and some deep learning algorithms are black boxes ; algorithms can predict with extreme precision, but offer little to no comprehensible explanation to the logic behind its decisions aside from the data and type of algorithm used.
The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI algorithms can also be used to analyze large amounts of data through electronic health records for disease prevention and diagnosis.
Additionally, hospitals are looking to AI software to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs. As widespread use of AI in healthcare is relatively new, there are several unprecedented ethical concerns related to its practice such as data privacy, automation of jobs, and representation biases.
Research in the s and s produced the first problem-solving program, or expert system , known as Dendral. The s and s brought the proliferation of the microcomputer and new levels of network connectivity.
During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians. Medical and technological advancements occurring over this half-century period that have enabled the growth healthcare-related applications of AI include:.
Various specialties in medicine have shown an increase in research regarding AI. Therefore there is a natural fit between the dermatology and deep learning. There are 3 main imaging types in dermatology: contextual images, macro images, micro images. Through imaging in oncology, AI has been able to serve well for detecting abnormalities and monitoring change over time; two key factors in oncological health. The Radiological Society of North America has implemented presentations on AI in imaging during its annual conference.
Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance. In , a paper published in the journal Annals of Oncology mentioned that skin cancer could be detected more accurately by an artificial intelligence system which used a deep learning convolutional neural network than by dermatologists.
On average, the human dermatologists accurately detected In January researchers demonstrate an AI system, based on a Google DeepMind algorithm, that is capable of surpassing human experts in breast cancer detection. In psychiatry, AI applications are still in a phase of proof-of-concept.
Challenges include the fact that many applications in the field are developed and proposed by private corporations, such as the screening for suicidal ideation implemented by Facebook in Primary care has become one key development area for AI technologies. An article by Jiang, et al. The increase of telemedicine , the treatment of patients remotely, has shown the rise of possible AI applications.
The information can be compared to other data that has already been collected using artificial intelligence algorithms that alert physicians if there are any issues to be aware of. Another application of artificial intelligence is in chat-bot therapy.
Some researchers charge that the reliance on chat-bots for mental healthcare does not offer the reciprocity and accountability of care that should exist in the relationship between the consumer of mental healthcare and the care provider be it a chat-bot or psychologist , though.
Since the average age has risen due to a longer life expectancy, artificial intelligence could be useful in helping take care of older populations. Electronic health records EHR are crucial to the digitalization and information spread of the healthcare industry. Improvements in natural language processing led to the development of algorithms to identify drug-drug interactions in medical literature. Efforts were consolidated in in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.
Deep learning algorithms have been developed to parse these reports and detect patterns that imply drug-drug interactions. DSP, a molecule of the drug for OCD obsessive-compulsive disorder treatment, was invented by artificial intelligence through joint efforts of Exscientia British start-up and Sumitomo Dainippon Pharma Japanese pharmaceutical firm.
The drug development took a single year, while pharmaceutical companies usually spend about five years on similar projects. DSP was accepted for a human trial. In September Insilico Medicine reports the creation, via artificial intelligence, of six novel inhibitors of the DDR1 gene, a kinase target implicated in fibrosis and other diseases.
The same month Canadian company Deep Genomics announces that its AI-based drug discovery platform has identified a target and drug candidate for Wilson's disease. The trend of large health companies merging allows for greater health data accessibility. Greater health data lays the groundwork for implementation of AI algorithms. A large part of industry focus of implementation of AI in the healthcare sector is in the clinical decision support systems.
As more data is collected, machine learning algorithms adapt and allow for more robust responses and solutions. The following are examples of large companies that have contributed to AI algorithms for use in healthcare:. MD use AI to give medical consultation based on personal medical history and common medical knowledge. Users report their symptoms into the app, which uses speech recognition to compare against a database of illnesses. Babylon then offers a recommended action, taking into account the user's medical history.
Entrepreneurs in healthcare have been effectively using seven business model archetypes to take AI solution[ buzzword ] to the marketplace. These archetypes depend on the value generated for the target user e. It also works in the field of medical imaging. The Indian startup Haptik recently developed a WhatsApp chatbot which answers questions associated with the deadly coronavirus in India.
With the market for AI expanding constantly, large tech companies such as Apple, Google, Amazon, and Baidu all have their own AI research divisions, as well as millions of dollars allocated for acquisition of smaller AI based companies. The use of AI is predicted to decrease medical costs as there will be more accuracy in diagnosis and better predictions in the treatment plan as well as more prevention of disease.
Other future uses for AI include Brain-computer Interfaces BCI which are predicted to help those with trouble moving, speaking or with a spinal cord injury. Artificial intelligence has led to significant improvements in areas of healthcare such as medical imaging, automated clinical decision-making, diagnosis, prognosis, and more. Although AI possesses the capability to revolutionize several fields of medicine, it still has limitations and cannot replace a bedside physician.
Healthcare is a complicated science that is bound by legal, ethical, regulatory, economical, and social constraints. In order to fully implement AI within healthcare, there must be "parallel changes in the global environment, with numerous stakeholders, including citizen and society. Artificial intelligence continues to expand in its abilities to diagnose more people accurately in nations where fewer doctors are accessible to the public. Many new technology companies such as Spacex and the Raspberry Pi Foundation have enabled more developing countries to have access to computers and the internet than ever before.
Using AI in developing nations who do not have the resources will diminish the need for outsourcing and can improve patient care. AI can allow for not only diagnosis of patient is areas where healthcare is scarce, but also allow for a good patient experience by resourcing files to find the best treatment for a patient. While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias , Do not resuscitate implications, and other machine morality issues.
These challenges of the clinical use of AI has brought upon potential need for regulations. Currently, there are regulations pertaining to the collection of patient data. In October , the group published The National Artificial Intelligence Research and Development Strategic Plan, outlining its proposed priorities for Federally-funded AI research and development within government and academia. The only agency that has expressed concern is the FDA. We already have some scientists who know artificial intelligence and machine learning, but we want complementary people who can look forward and see how this technology will evolve.
As of November , eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions. In order to effectively train Machine Learning and use AI in healthcare, massive amounts of data must be gathered. Acquiring this data, however, comes at the cost of patient privacy in most cases and is not well received publicly.
Though if AI were to automate healthcare related jobs, the jobs most susceptible to automation would be those dealing with digital information, radiology, and pathology, as opposed to those dealing with doctor to patient interaction. Automation can provide benefits alongside doctors as well. It is expected that doctors who take advantage of AI in healthcare will provide greater quality healthcare than doctors and medical establishments who do not.
AI may avert healthcare worker burnout and cognitive overload. AI will ultimately help contribute to progression of societal goals which include better communication, improved quality of healthcare, and autonomy.
Since AI makes decisions solely on the data it receives as input, it is important that this data represents accurate patient demographics. In a hospital setting, patients do not have full knowledge of how predictive algorithms are created or calibrated.
Therefore, these medical establishments can unfairly code their algorithms to discriminate against minorities and prioritize profits rather than providing optimal care. There can also be unintended bias in these algorithms that can exacerbate social and healthcare inequities.
White males are overly represented in medical data sets. For instance, HIV is a prevalent virus among minority communities and HIV status can be used to discriminate against patients. From Wikipedia, the free encyclopedia.
Overview of the use of artificial intelligence in healthcare. Artificial intelligence Glossary of artificial intelligence Full body scanner ie Dermascanner , Harvard Business Review. Retrieved Guide to medical informatics, the Internet and telemedicine. Massachusetts General Hospital. Artificial Intelligence. Readings in medical artificial intelligence: the first decade. Addison-Wesley Longman Publishing Co. Bibcode : Sci Journal of the American Medical Informatics Association.
Methods of Information in Medicine. Computer Methods and Programs in Biomedicine.
Deep learning-enabled medical computer vision
Murphy, editors. Machine learning in radiation oncology :. Murphy, editors Material Type :. Cham :: Springer,, Includes bibliographical references and index.
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Machine learning in radiation oncology : theory and applications
It seems that you're in Germany. We have a dedicated site for Germany. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology.
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