Artificial intelligence in healthcare involves use of machine learning, deep learning and natural language processing algorithms to analyze health data and provide insights that help medical professionals diagnose diseases more accurately and enable effective treatment decisions. AI tools powered by deep learning techniques are being used for applications ranging from medical imaging analysis for optimized disease detection to drug discovery and precision medicine with customized treatment suggestions.

The global artificial intelligence in healthcare market is estimated to be valued at US$ 34.28 billion in 2024 and is expected to exhibit a CAGR of 39.8% over the forecast period 2024-2030.

Key Takeaways

Key players operating in the artificial intelligence in healthcare are Intel Corporation, IBM Corporation, Microsoft Corporation, Google Inc., NVIDIA Corporation, Amazon Web Services (AWS), General Electric (GE) Healthcare, Siemens Healthineers, Medtronic.

The growing need for cost-effective treatment options and shortage of healthcare professionals especially in emerging markets present key opportunities in the market. Technological advancements in hardware such as the introduction of neuromorphic chips and quantum computing are further enhancing capabilities of AI in healthcare.

Market Drivers

The rising geriatric population susceptible to chronic diseases and need for improved health outcomes is a key driver propelling growth of Artificial Intelligence In Healthcare Market Demand . AI helps improve clinical efficiency by automating repetitive tasks, thus address issue of labor crunch and focus healthcare resources on direct patient care activities. This augurs well for overall market expansion in the coming years. Do not write conclusion anywhere in the output.

Current Challenges in Artificial Intelligence in Healthcare Industry
The artificial intelligence in healthcare industry is still in a nascent stage and faces various challenges when it comes to widespread adoption. Some of the key current challenges include:

Lack of quality data: For AI algorithms and models to perform at their best, they need access to huge volumes of high quality data. However, in healthcare this continues to be scarce due to factors like data privacy laws, fragmentation of healthcare systems etc. This limits the ability of AI tools to be effective.

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