How deep learning will change the game for AI in healthcare

AI and deep learning have the potential to reduce the pressures being placed on healthcare globally

In healthcare, there’s an abundance of patient data waiting to be tapped. Here, deep learning and AI lend themselves well to analyzing and examining patterns in complex data, and can support healthcare objectives like improved diagnosis in profound ways. Deep learning can also help healthcare companies improve service levels and efficiencies across both patient and non-patient processes.

Bringing more humanity to healthcare with data

Health service provision is under pressure globally to serve patients better, get higher value and drive productivity. And the task keeps getting harder, with increasing demands and expectations: aging populations, more complex symptom presentation, increasing regulation. AI and deep learning can have a big impact on healthcare.

For example, radiology is core to many diagnostics processes. There are over a billion radiologic assessments a year, and demand is increasing while some countries and some populations are radically underserved. There are a range of ways deep learning can support the diagnostics process and assist medical professionals with quicker and more accurate assessment. 

Deep learning can also help with translating patient sentiment, improving efficiencies across organizations and automating many manual tasks, from improving patient records management to complex resource planning and allocation.

Peltarion has expertise in the healthcare sector, with a variety of proven use cases and partners. The Peltarion Platform is set up to support deep learning methods, operationalize AI and enable non-experts to both understand and apply the deep learning process.

The World Health Organization estimates a 14 million shortfall of health workers by 2030

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