- NLP/Conversational AI
- Cognitive computing
- Robotic process automation (RPA)
- Wearables - IoT sensors
- Clinical Decision Support
- Medical Imaging Interpretation
- AI in workflow
- AI in clinical trials
- AI drug design
- Foundation model
- Knowledge graphs
- Digital twins
- Machine learning and deep learning
This section explores natural language processing, a subfield of artificial intelligence concerned with the interactions between computers and human language.
The authors first discuss the obvious observation of this domain: interest in application of AI tools in clinical medicine and healthcare continues to escalate, but its widespread adoption from academic research into deployable AI devices has been difficult.
As we come to the close of 2021, it is a good time to project ahead for artificial intelligence in medicine and think about the future of its role in transforming healthcare.
The COVID-19 pandemic has really shaken healthcare as it was, and perhaps this is a grand opportunity for us to reshape the future of healthcare with artificial intelligence.
This section covers cognitive computing – self-learning systems that use data mining techniques, pattern recognition, natural language and human senses processing, and system refinements based on real time acquisition of patient and other data.
In the last newsletter, we discussed examples of machine-to-machine or model-to-model collaborative learning in the form of meta models used in machine learning ensemble methods such as bagging (parallel), boosting (sequential), and stacking.
Life sciences researchers are under pressure to innovate faster than ever. Big data offer the promise of unlocking novel insights and accelerating breakthroughs. Ironically, although more data are available than ever, only a fraction is being integrated, understood, and analyzed.
A high percentage of medical errors, committed because of physician’s lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques.
Robotic process automation (RPA)
This section investigates robotic process automation (RPA), the technology that deploys particular rules and algorithms, then automates tasks with minimal error and enhances productivity.
AI research, and more specifically research that utilizes deep learning, has been a hot topic for roughly the past decade. However, for those not already familiar with this field, it may look like a complete morass, or a black box
AI is transforming industries around the world and revolutionizing the field of health care. The possibilities are limitless – if we get it right. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system and from different sources.
This is an excellent and timely opinion piece in Frontiers of Digital Health which opines on the difference between artificial intelligence and statistics in the published healthcare literature. It is interesting to note that it is not merely the number of publications with the term artificial intelligence that has increased
Wearables - IoT sensors
This section explores the possibilities of wearable technologies, Internet of Things (IoT) sensors and remote patient monitoring (RMP) smart devices, that enable the continuous monitoring of human physical activities and behaviors, as well as physiological and biochemical parameters.
Following another recent American Board of AI in Medicine (ABAIM) introductory course with some very enthusiastic attendees, I decided to treat myself to the inaugural Miami Formula One (F1) race. The data science and race strategy of F1 racing can inspire us to improve data science and artificial intelligence in healthcare.
Daniel O’Hair reflects on the clinical and medical impact of AI solutions implemented at Boulder Community Health.
Tom Burton and a group of healthcare veterans started Salt Lake City-based Health Catalyst in 2008. Initially, they wanted to revolutionize clinical process models using analytics. But during development, they were challenged by the quest for a data warehouse that could handle the complexity of healthcare data.
Clinical Decision Support
This section explores clinical decision support tools – resources designed to support clinical decision-making.
Medical Imaging Interpretation
This section explores AI tools that are used to automatically recognize complex patterns in imaging data and provide quantitative assessments of radiographic characteristics.
AI in workflow
This section explores workflow automation – AI tools that optimize processes by replacing manual tasks with software that executes all or part of a process.
AI in clinical trials
This section explores the many potential applications of AI in clinical trials.
AI drug design
This section explores the range of AI tools that could help tackle the development of more-effective and better-targeted drugs.
This section covers foundation models – algorithms that train and develop with broader datasets to execute various functions and give rise to new capabilities for efficiently implementing tasks.
This section explores knowledge graphs. Knowledge graphs offer a way to integrate information extracted from multiple data sources.
The deep learning gurus of Bengio, LeCun, and Hinton, in the article reviewed last week, mentioned that the future of deep learning will need to address the myriad of shortcomings. The Ahmedt-Ariztizabal article is an interesting followup, therefore, to last week’s deep learning article as it addresses one of these limitations of deep learning.
This section examines the concept of a digital twin, a highly complex virtual model that is the exact counterpart (or twin) of a physical thing.
This section covers the importance of people within the realm of AI tools and deployment.
Machine learning and deep learning
This section explores machine learning and deep learning – types of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
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Riverain TechnologiesCategory: Imaging
Riverain Technologies is a medical software innovator that develops AI based solutions to aid radiologists in the early detection of disease. With the use of Riverain’s ClearRead X-ray Suite and ClearRead CT, radiologists are able to optimize the use of existing imaging and reading workstation equipment. This enables radiologists to better utilize their diagnostic expertise in image interpretation for identification of diseases, such as lung cancer.+1 800 990 3387https://www.riveraintech.com/contact/
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