Generative AI is poised to have a transformative impact on society and the workforce across several dimensions:

1. Automation and Efficiency: Generative AI can automate routine and repetitive tasks, which could lead to increased efficiency in various industries. For example, in journalism, AI can write standard reports, allowing human journalists to focus on in-depth investigative work. In design, AI can generate initial models, speeding up the creative process.
2. Job Displacement and Creation: While there is a concern that AI could displace jobs, particularly in sectors reliant on routine tasks, it also has the potential to create new types of jobs. These could include roles in AI management, oversight, and development, as well as new opportunities in industries that harness AI to offer new services or products.
3. Education and Skill Development: The rise of generative AI emphasizes the need for a workforce skilled in AI literacy, data analysis, and ethical considerations surrounding AI use. Educational systems may need to adapt to prepare individuals for these new demands, focusing more on critical thinking, creativity, and technical skills.
4. Ethical and Societal Considerations: Generative AI raises significant ethical questions, such as biases in AI outputs, the potential for misuse (e.g., deepfakes), and privacy concerns. Addressing these issues will require robust regulatory frameworks and ongoing dialogue among stakeholders to ensure that AI is used responsibly.
5. Economic Impact: AI could drive economic growth by creating new market opportunities and increasing productivity. However, it could also exacerbate economic disparities if the benefits of AI are unevenly distributed or if there is a significant divide between those who have the skills to work with AI and those who do not.
6. Cultural and Creative Fields: In creative sectors, generative AI can be both a tool and a collaborator, helping to push the boundaries of art, music, and literature. However, it also prompts discussions about the nature of creativity and originality.
7. Healthcare: In healthcare, AI can help diagnose diseases more accurately and quickly, recommend personalized treatment plans, and automate administrative tasks, potentially improving care outcomes and efficiency.

The impact of generative AI will be broad and multifaceted, requiring proactive adaptation from individuals, businesses, and governments to harness its benefits while mitigating its risks.

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Using data from China and New York, the new mobile app, which has been developed by researchers NYU College of Dentistry, works to help clinicians identify which COVID-19 patients are most at risk of suffering a high severity of the disease. The Artificial Intelligence (AI) is used to help the clinicians assess the risk factors and identify biomarkers from blood tests.

The findings have been published Royal Society of Chemistry journal Lab on a Chip.

Technology supporting the fight against COVID-19

This new mobile app could be a vital tool in the fight against COVID-19 as current tests only test whether someone does or does not have the virus, not how sick they may become.

Lead researcher John McDevitt, professor of biomaterials at NYU College of Dentistry, said: “Identifying and monitoring those at risk for severe cases could help hospitals prioritise care and allocate resources like ICU beds and ventilators. Likewise, knowing who is at low risk for complications could help reduce hospital admissions while these patients are safely managed at home.

“We want doctors to have both the information they need, and the infrastructure required to save lives. COVID-19 has challenged both of these key areas.”

Artificial Intelligence COVID-19 ‘severity score’

To develop the app data from 160 patients, who had COVID-19 in a hospital in Wuhan, China, was used to help identify different biomarker in blood tests. The researchers then built a model using the information and trained the AI with a machine learning algorithm to define the patterns of COVID-19 and predict its severity – creating a severity score from 0 (mild) to 100 (critical).

The model was validated using data from 12 hospitalised COVID-19 patients from Shenzhen, China, and 1,000 New York City COVID-19 patients, which confirmed that the model’s severity scores were significantly higher for the patients that died versus those who were discharged.

Supporting clinical decisions

Co-author Isaac Dapkins, chief medical officer for the Family Health Centers at NYU Langone, said: “Real time clinical decision support tools for COVID-19 can be extremely helpful, particularly in the outpatient setting, to help guide monitoring and treatment plans for those at greatest risk.”

“We hope this tool can help identify those at high risk for adverse outcomes and reduce the health disparities present with COVID-19,” added Larry McReynolds, executive director for the Family Health Centers at NYU Langone.

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