As quality data and consumer and patient trust increase, AI will become more reliable. It’s only a matter of time before a computer can be more accurate in making a diagnosis and determining treatment. As such, it’s not a question of if this will happen, but when and how.
And the more trust increases, the faster AI will develop. This lack of trust is a self-fulfilling prophecy of reduced effectiveness – the more AI is used (the more practice it gets), the better it can become. However, physicians may be reluctant to implement computer diagnosis tools for fear of being replaced. And before we reach the point of trusting AI diagnoses, regulations need to be in place to enable mistakes to be tracked down.
But players in the healthcare industry have already begun using AI-apps into their operations and scanning devices. The technology is used to acquire, collect, and organize accurate medical images of a patient’s history, as well as the patient’s scans. AI is also being used for imaging diagnosis in genetics, labs, pathology, and other healthcare areas as it facilitates decision-making and improves processes and diagnosis. Last but not least, AI helps with diagnosis as it enables decisions to be made based on the data.
There are significant benefits and several opportunities AI, and machine learning will bring to the healthcare industry. It won’t be simply optimizing monetary transactions or predicting the fastest possible route, but helping doctors to find the best treatment. Or helping to reduce the burdens put upon patients by being more precise with treatments and treatment decisions. AI and digitalization are playing an important part in:
Expanding precision medicine
- Diagnostic precision through quantitative imaging
- Personalization with intelligent image acquisition
Transforming care delivery
- Increased workforce productivity through assistance in automation
- Clinical operations optimization
Improving patient experience
- Prioritizing complex/acute cases and
- Avoiding unnecessary interventions
Highly important for the use of AI in healthcare is clear guidance for its implementation. That way, companies can stay ahead of legal, ethical, and regulatory issues associated with collecting and using data. Privacy by design and by default should be the key principles driving developments. According to Jörg Aumüller, leader of the Digital Health global marketing team at Siemens Healthineers, these high standards are essential to the development of AI-powered solutions.