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Artificial intelligence has the potentiality to completely refurbish healthcare system. AI algorithms can mine medical records, build treatment plans, and create medications faster than any other ways in the healthcare ecosystem, including doctors.
For decades, artificial intelligence has been a key factor in industry. AI has just started a leading role in medical care in recent times. There is an estimate that AI systems will be an industry of $6 billion by 2021. A recent forecast that healthcare is one of the 5 top industries with over 50 use cases involving IA, and more than $1 billion in start-up equity.
AI technologies could win the battle for us against cancer, AIDS or Ebola – and could simply lead to healthier individuals living in healthier communities.
What is AI? What is AI?
In the 1950s, artificial intelligence was initially designed to help a computer or machine think and learn as people do. AI is used by firms such as Facebook and Google (e.g. who is in a photo) (e.g. providing search suggestions, or identifying the fastest route to drive). But AI has taken only small steps towards an extensive and multidimensional opportunity in the healthcare industry.
AI is proving to be a game-changer in the healthcare industry in a variety of ways. Below are some examples that are currently being used:
Radiology: In order to automate image analysis and diagnosis, AI solutions are being developed. This may help show areas of concern for a scan, drive efficiency and decrease human error for a radiologist. Fully automated solutions – reading and interpreting a scan automatically without being monitored by human beings – are also provided that can allow for instant interpretation. Recent evidence of better tumour detection in MRIs and CTs shows how new possibilities for cancer prevention have been developed. In the meantime, a US firm has already received FDA clearance for the analysis and interpretation of cardiac MRI images of an AI powered platform.
Drug Discovery: New potential therapies are being developed from a broad database of information on current medicines to tackle critical threats such as the Ebola virus and be redesigned to identify these threats. This could improve the efficiency and success of drug development and speed up the process of marketing new drugs in response to the threats of deadly diseases.
Identification of Patient Risks: AI solutions can provide clinicians with real-time aid in identifying risk patients by analysing large amounts of historical patient data. Other recent work has shown that cardiovascular disease risks can be predicted based purely on the still picture of the retina of a patient.
Primary care: Many organisations work directly on patient data and provide advice by means of voice or chat. This provides fast and scalable access to basic and medical questions. It could help avoid unnecessary trips to the GP, reduce increasing demand on primary healthcare providers, and provide basic advice, in a subset of conditions, that populations in remote or under-served areas would otherwise not be able to access. Although the concept is clear, these solutions still require extensive independent validation to demonstrate safety and effectiveness for patients.
McKinsey estimates that using big data, as well as artificial intelligence and machine learning tools to process it, could result in $100 billion in annual savings for medicine and pharma.
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