It has been revealed that almost $1bn was invested in AI-focused healthcare start-ups in the first quarter of 2020 alone and recent projections show the global industry growing at a rate of 44% until 2026.
From 3D image analysis to robotic surgery, AI is now entrenched in the mass market and is becoming a part of everyday life in healthcare. While the COVID-19 pandemic has brought with it enough hardship, it has also catalysed technological developments and awareness in the field of healthcare. It has been revealed that almost $1bn was invested in AI-focused healthcare start-ups in the first quarter of 2020 alone and recent projections show the global industry growing at a rate of 44% until 2026.
At its core, AI is machine learning and constitutes three cognitive nodes: computer vision, natural language processing and data inference. 2020 has seen exciting developments for healthcare in all three areas. In 2020, the healthcare AI industry reached a tipping point and has finally become mainstream.
Natural language processing, for instance, has been in the spotlight during the pandemic as many healthcare providers have been forced to operate remotely. The ‘telehealth’ industry has grown significantly because of its ability to allow physicians to automate and streamline basic services and free up resources to deal with the crisis.
Artificial Intelligence does not have a single, fool proof blueprint for its implementation. Healthcare organisations looking to harness its vast potential and many possible applications must make choices that fit their financial and technical capabilities.
The number one question that providers must ask themselves before embarking on their AI journey is if they have the capacity to build the solutions in-house. While having the internal resources, proprietary data and capital to develop AI solutions in-house has obvious benefits in terms of control, businesses have to decide for themselves whether it’s realistic given their goals and timeline.
The next important question to be considered is regarding partnerships vs. acquisitions. Even with the best resources and in-house capabilities, partnerships can be very beneficial and can rapidly increase the development and deployment of AI systems and tools. Investments in AI start-ups or acquisitions of smaller companies can also give an organisation fast access to development phases and offer greater expertise and capabilities.
Lastly, businesses will need to think about the key enablers that will accelerate their AI strategy. This involves thinking about things like building or acquiring new technologies, leadership alignment, team allocation and more.
Data is AI’s reason to be. Without a continuous supply of data, AI technology will cease to exist. However, ‘dirty data’, which is not yet standardised and remains disparate, can be a nuisance for organisations. Privacy protocols and security requirements also pose a challenge to progress but they must be overcome because they revolve around the protection of patient rights. Patient consent regarding the use of their data and the perceived bias in algorithms are also ethical issues that need to be addressed.
These challenges are tricky to overcome, but it is not impossible. AI is not just an innovation that is worth pursuing, but one that will transform the healthcare industry for the better. In order for organisations to successfully implement AI, it is essential that leadership is on board and the right talent is being supported. Whether AI in healthcare thrives or fails is reliant on healthcare organisations across the world.