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A potential new ESG evaluation framework and scoring methodology is being developed for corporate issuers (referred to herein as the 'ESG assessment tool' or the 'tool'), and S&P Global Ratings is seeking feedback.

The methodology is to evaluate a company's impact on the natural and social environments it inhabits, the governance mechanisms it has in place to oversee those effects, and potential losses it may face as a result of its exposures to such environmental and social risks.

This reflects our commitment to transparency in the way in which we consider ESG factors when determining credit ratings, and support for industry efforts to encourage consistent public disclosure by issuers on ESG factors that may impact creditworthiness.

We are considering basing our ESG assessments on four main pillars, or subfactors: The proposed framework would also consider incorporating a mitigation history modifier for subfactors A and B, so we could differentiate a company with a strong environmental and social mitigation history over a given time period (we are proposing the past 10 years, on a rolling forward basis) from one with weaker mitigation risk, as seen in the number of E and S related adverse events during that period.

Cedefop

project analyses the impact and drivers of automation, robotics, artificial intelligence and other digital technologies on employment and changing skill needs of jobs.

Artificial intelligence in healthcare

Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate 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 it 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.

to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs.[8]

that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.[9]

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.[14]

The ability to interpret imaging results with radiology 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.[25]

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.[26][27]

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.[28][29]

In 2018, 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 86.6% of skin cancers from the images, compared to 95% for the CNN machine.[30]

One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline.[citation needed]

To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature.

Efforts were consolidated in 2013 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.[40]

Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports.[36][37]

The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility.[43]

A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.[53]

Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.[54]

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.[64]

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).[65]

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.

Virtual nursing assistants are predicted to become more common and these will use AI to answer patient's questions and help reduce unnecessary hospital visits.

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.

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.”[75]

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.[77][78]

| Innovation speaker hails artificial intelligence as vital to water sector

who sold water pumps and performed data modeling early in his career, said the artificial intelligence of computers – in concert with the natural intelligence of humans – can take innovation a step further by analyzing streams of data to improve future outcomes. “Utilities

example, using data from sources such as meters, labs, SCADA (supervisory control and data acquisition), process models, and GIS (geographic information systems), artificial intelligence can predict a developing water quality issue and recommend measures to resolve it before it happens.

Also, utilities are coming from a long and successful history of proven practices developed through natural intelligence, so many are slower to adopt new artificial intelligence technology, especially if there aren’t many successful examples to assess. To

estimates that by embracing the use of artificial intelligence, water utilities could save as much as $11 billion a year for their communities, based on his analysis of large utilities in the U.S. water sector. “When

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