AI News, Artificial Intelligence
Business Opportunities in Emerging Technologies: Artificial Intelligence
Once the stuff of futuristic science fiction movies, artificial intelligence has moved from the big screen to, well… any screen.
Business Opportunities in Emerging Technologies: Artificial Intelligence, a white paper authored by CompTIA’s Emerging Technology Community, will provide technology solution providers with a better understanding of both artificial intelligence technology and the market opportunities that exist today, as well as how to capitalize on those opportunities.
6 Ideal Ways Artificial Intelligence Reinvents Human Resources
AI is omnipresent becoming the norm in many facets of our everyday lives.
Instead of spending so much time debating how AI is replacing our jobs, we easily forget that these same technologies have a major role in recruiting, securing, and employee retention.
But the pace is quickening — AI is set to take the center stage in most organizations — intelligent machines demonstrating their capability to reduce costs, boost employee productivity, and keep businesses on their toes.
👉And half of the 🔗HR professionals said they recognized cognitive computing as a powerful tool in transforming key dimensions of human resource.
Smart people analytics It’s been years together collecting data on their customers to gain positive insights for future prediction says, Guarino.
Saving time allows human resource professional to widen their focus toward value-add work such as continuous feedback and mentoring.
AI tools can easily automate common human resource tasks such as benefits management and common questions requests, etc.
Identify employees looking to pave their way out At Veriato, employers use an AI platform that can easily screen employees that are looking to head out from the company.
The AI system can detect changes in the overall behavior of employees’ communication, thus predicting when the employee plans to take an exit from the organization.
On a daily basis, they need to keep a check on handling tasks like vacation requests, team training, hiring processes, and determining mood change.
No doubt, due to advancement in AI, human resources now finds it easier to lead an unbiased and fast-track measures in accelerating workforce development.
Risks and remedies for artificial intelligence in health care
Artificial intelligence (AI) is rapidly entering health care and serving major roles, from automating drudgery and routine tasks in medical practice to managing patients and medical resources.
Providers spend a tremendous amount of time dealing with electronic medical records, reading screens, and typing on keyboards, even in the exam room.4 If AI systems can queue up the most relevant information in patient records and then distill recordings of appointments and conversations down into structured data, they could save substantial time for providers and might increase the amount of facetime between providers and patients and the quality of the medical encounter for both.
Training AI systems requires large amounts of data from sources such as electronic health records, pharmacy records, insurance claims records, or consumer-generated information like fitness trackers or purchasing history.
Some patients may be concerned that this collection may violate their privacy, and lawsuits have been filed based on data-sharing between large health systems and AI developers.6 AI could implicate privacy in another way: AI can predict private information about patients even though the algorithm never received that information.
For instance, if the data available for AI are principally gathered in academic medical centers, the resulting AI systems will know less about—and therefore will treat less effectively—patients from populations that do not typically frequent academic medical centers.
Similarly, if speech-recognition AI systems are used to transcribe encounter notes, such AI may perform worse when the provider is of a race or gender underrepresented in training data.7 “Even if AI systems learn from accurate, representative data, there can still be problems if that information reflects underlying biases and inequalities in the health system.”
For example, African-American patients receive, on average, less treatment for pain than white patients;8 an AI system learning from health-system records might learn to suggest lower doses of painkillers to African-American patients even though that decision reflects systemic bias, not biological reality.
Some scholars are concerned that the widespread use of AI will result in decreased human knowledge and capacity over time, such that providers lose the ability to catch and correct AI errors and further to develop medical knowledge.9 The nirvana fallacy.
One set of potential solutions turns on government provision of infrastructural resources for data, ranging from setting standards for electronic health records to directly providing technical support for high-quality data-gathering efforts in health systems that otherwise lack those resources.
However, many AI systems in health care will not fall under FDA’s purview, either because they do not perform medical functions (in the case of back-end business or resource-allocation AI) or because they are developed and deployed in-house at health systems themselves—a category of products FDA typically does not oversee.
Increased oversight efforts by health systems and hospitals, professional organizations like the American College of Radiology and the American Medical Association, or insurers may be necessary to ensure quality of systems that fall outside the FDA’s exercise of regulatory authority.10 “A hopeful vision is that providers will be enabled to provide more-personalized and better care.
A hopeful vision is that providers will be enabled to provide more-personalized and better care, freed to spend more time interacting with patients as humans.11 A less hopeful vision would see providers struggling to weather a monsoon of uninterpretable predictions and recommendations from competing algorithms.
- On 5. marts 2021
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