AI News, Intelligent Health AI 2019
Artificial intelligence in healthcare
Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to estimate human cognition in the analysis of complicated medical data.
What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user.
AI algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can't adjust itself and only understand what is has been told explicitly, (2) and algorithms are black boxes;
AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care.
Additionally, hospitals are looking to AI solutions to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs.
are developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.
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.
An ability to interpret imaging results may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss.
A study at Stanford created an algorithm that could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial.
The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can achieve improvements in certain statistical metrics in isolated cases, as opposed to specialists.
Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft patients therapy in regard to facial attractiveness or age appearance.
One study conducted by the Centerstone research insititute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline.
The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility.
A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.
Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.
identRx is the first fully automated medication verification and dispensing device, using AI to identify and verify pill in real time, with an accuracy greater than 99%.
As an autonomous, AI-based system, IDx-DR is unique in that it makes an assessment without the need for a clinician to also interpret the image or results, making it usable by health care providers who may not normally be involved in eye care.
team associated with the University of Arizona and backed by BPU Holdings began collaborating on a practical tool to monitor anxiety and delirium in hospital patients, particularly those with Dementia.
The AI utilized in the new technology – Senior's Virtual Assistant – goes a step beyond and is programmed to simulate and understand human emotions (artificial emotional intelligence).
Doctors working on the project have suggested that in addition to judging emotional states, the application can be used to provide companionship to patients in the form of small talk, soothing music, and even lighting adjustments to control anxiety.
Overall, as Quan-Haase (2018) says, technology “extends to the accomplishment of societal goals, including higher levels of security, better means of communication over time and space, improved health care, and increased autonomy” (p. 43).
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.
As of November 2018, 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.
Artificial Intelligence for Good
Intellectual Ventures Laboratory and Global Good are focusing on ways to use Artificial Intelligence to address common healthcare infrastructure problems in low and middle income countries where a lack of trained medical professionals and hospital infrastructure are the cause of most poor health outcomes.
More about Benjamin Wilson and the Center for Intelligent Devices: The Center for Intelligent Devices focuses on translational hardware and software research for global health and global development applications, with a focus on machine learning and artificial intelligence algorithms.
Is Artificial Intelligence The Answer To A Plethora of Healthcare Problems?
The world of healthcare is changing, and with it, our approach to understanding the concept of patients and doctors, ways of delivering care, and building a better relationship between every member associated with it is also changing.
According to a report, the overall AI market is projected to grow from 2016 at a compounded annual growth rate of 62.9%, reaching $16.6 billion by 2022 and AI for healthcare is expected to be a significant driving force.
From leveraging hundreds of arms into performing one task, they remodeled the process into utilizing few hands performing multiple activities.
For a sector that is aiming at reducing costs and improving efficiency, healthcare has a number of opportunities where AI can be an important part, such as: –
From evaluating job applicants on their emerging strengths to creating a self-service AI platform that learns from analytics, machine learning is a fascinating avenue.
Whether it be predicting the future cost of care of patients or addressing the social determinants of health, Artificial Intelligence and Machine Learning can open new ways to achieve the best clinical and non-clinical outcomes.
What we need now is to understand its need, analyze the space, and implement proven strategies to build a healthcare world where the best technologies are within the reach of providers and are aimed to deliver the best care that our patients deserve.
In his role as the CEO, Abhinav has laid the foundation for Innovaccer’s success as a leading data activation platform company and registering a 400% y-o-y growth.
Harnessing the power of AI to transform healthcare
One of the many remarkable things about artificial intelligence is that while we tend to think of it as something that will have a big effect in the not-too-distant future, it is already transforming people’s lives in profound and powerful ways today.
In the U.S., researchers are exploring how AI can help public health organizations around the world prevent the spread of deadly diseases like Ebola, Chikungunya, and Zika by detecting the presence of pathogens in the environment and stopping transmission to humans before outbreaks can begin.
To do this, a team of Apollo clinicians and data scientist started by reviewing more than 400,000 patient records from its hospitals around the country and found that nearly 60,000 patients had suffered a cardiac event after a health checkup.
In China, Ray Zhang, CEO of a startup company called Airdoc, recruited a team of engineers to develop an AI-based diagnostic tool that can instantly detect signs of chronic illnesses including diabetes, hypertension, arteriosclerosis, age-related macular degeneration, and more – simply by taking a high-resolution image of the back of the eye.
To create it, the Airdoc team used thousands of retinal scans to create an algorithm using Microsoft Azure’s machine learning capabilities that is trained to look for tiny abnormalities such as specks, spots, and deformed blood vessels that can be warning signs for a wide range of health issues.
The algorithm then automatically adjusts the angle until a green cross comes into focus and captures a high-resolution, medical-grade image that is instantly uploaded to the cloud, where it takes less than a second to conduct a detailed analysis that rates susceptibility to a long list diseases as either low, medium, or high.
We’re also working with the Princess Margaret Cancer Center at University Health Centre in Toronto to redefine cancer treatment through a remarkable new approach called “single cell sequencing” that enables doctors to analyze the genetic makeup of every single cell in a cancerous tumor and then select a combination of drugs that is optimized to kill the greatest number of cancer cells.
By utilizing the power of Microsoft Azure Machine Learning and the cloud, single cell sequencing is enabling doctors to predict how every cell will respond to each of the thousands of compounds that are available for cancer treatment and then create a truly personalized therapy based on the specific genetic characteristics of each cancerous tumor.
- On 21. oktober 2021
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