AI Tools
and Deployment

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AI Tools and Deployment

There is a growing portfolio of technological tools that are available in the domain of AI in medicine, and more are being developed all the time. Consider use cases and lessons learned to aid your deployment decisions and benefit from the transformational opportunities that AI can provide.
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  • NLP/Conversational AI

    1. NLP

    This section explores natural language processing, a subfield of artificial intelligence concerned with the interactions between computers and human language.

    2. Clinical AI research

    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.

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    3. Future applications

    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.

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  • Cognitive computing

    1. Cognitive computing

    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.

    2. Cognitive elements

    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.

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    3. Applied to Big Data

    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.

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    4. Review of Fuzzy Cognitive Maps

    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.

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    5. Analytics and Algorithms

    PREFACE This book with the accompanying compendium is designed for anyone who is interested in a comprehensive primer on the principles and application of artificial intelligence in healthcare and medicine.

    Click here to read more

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  • Robotic process automation (RPA)

    1. 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.

    2. How to judge

    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

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    3. Limitless

    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.

    Click here to read more

    4. Statistics

    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

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  • Wearables - IoT sensors

    1. Wearables

    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.

    2. Data-driven

    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.

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    3. Innovative

    Daniel O’Hair reflects on the clinical and medical impact of AI solutions implemented at Boulder Community Health.

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    4. Transformation

    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.

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  • Clinical Decision Support

    1. Clinical Decision Support

    This section explores clinical decision support tools – resources designed to support clinical decision-making.

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  • Medical Imaging Interpretation

    1. 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.

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  • AI in workflow

    1. 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.

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  • AI in clinical trials

    1. Clinical trials

    This section explores the many potential applications of AI in clinical trials.

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  • AI drug design

    1. Drug design

    This section explores the range of AI tools that could help tackle the development of more-effective and better-targeted drugs.

    • Discovery
    • Repurposing
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  • Foundation model

    1. Foundation model

    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.

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  • Knowledge graphs

    1. Knowledge graphs

    This section explores knowledge graphs. Knowledge graphs offer a way to integrate information extracted from multiple data sources.

    2. Graph-based deep

    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.

    Click here to read more

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  • Digital twins

    1. Digital twins

    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.

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  • People

    1. People

    This section covers the importance of people within the realm of AI tools and deployment.

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  • Machine learning and deep learning

    1. ML, DL, & CNN

    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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Our top picks

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Company Directory

  • Riverain Technologies

    Category: Imaging
    Riverain Technologies

    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 3387
    https://www.riveraintech.com/contact/
    Website http://www.riveraintech.com

  • Want to know more?

    Category: AiMed
    Want to know more?

    Our content hubs have been developed to ensure that it is easy for payers and providers to access the specific resources that they need. If you would like to see your organisation featured here or would like to contribute some educational content on one of the topics shown above, please don’t hesitate to get in touch.

    The hubs will be regularly updated with more content so be sure to check back often!

    [email protected]
    [email protected]
    Website https://ai-med.io

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