PwC estimates that the impact of AI/Ml on the global economy could be greater that $15 trillion by 2030! ⬇️
No other technology is expected to have such an impact in the near future, particularly when it comes to healthcare. Where is AI/ML technology headed over the next few years? 🦾
Experts agree that the healthcare industry is poised to experience a game-changing transformation through AI/ML.
Learn the Top 5 trends for AI/ML in 2023, and where healthcare fits in:
1️⃣ Deep Learning supports new levels of data analysis
Deep Learning is a subset of ML and is a way for machines to mimic the human brain by creating neural networks to intelligently process huge amounts of data, beyond human capacity. ML algorithms then analyze data inputs and provides insights based on business goals.
2️⃣ Natural Language Processing (NLP) drives new use cases for AI/ML
NLP is a way for computers to understand and speak human language (text and visually). NLP can determine sentiment of text, classify and group text, as well as pictures and other forms of human communication, to make informed decisions about products, customers, markets, machinery, security and healthcare.
3️⃣ AI/ML positioned to transform the Healthcare Industry
Yes, the benefits of AI/ML cross all markets and industries. Arguably, nowhere is the potential for AI/ML to be more transformative that in healthcare. Largely the result of the pandemic, healthcare saw the dramatic rise in telemedicine and other technological advances. As one example, AI/ML can help fill the shortage of nurses and healthcare workers by introducing automation, increasing productivity and improving quality of care based on targeted ML algorithms and data science.
4️⃣ Augmented Intelligence improves human decision-making
Unlike science fiction movies where AI robots take over the world, many experts believe the greatest value garnered from AI/ML technology will be in providing deep insights that will enhance human decision-making, rather than replacing the human element altogether.
5️⃣ TinyML on the Edge
TinyML refers to any machine learning algorithm that can operate on some kind of embedded device or edge device (e.g., smartphone), in an IoT system. Edge devices, hardware that controls data flow between networks, have limited computing power. So, TinyML algorithms were developed to operate with little local memory or computing power, thus addressing both cost/consumption and data collection concerns.
AI/ML technology will continue to change our lives!
Do any of the 5 Trends resonate with your organization? We would love to hear from you! 📝
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