Artificial intelligence (AI) and machine learning (ML) continues to be in the spotlight in the healthcare industry, with a number of transformational applications of AI and ML in different sectors of healthcare industry being recently announced.
On 20 May, UK Prime Minister Theresa May announced plans to invest millions in government funds on a new AI strategy plan for early-stage cancer and chronic disease diagnosis. This plan aims to cut down the number of deaths from prostate, ovarian, lung, and bowel cancer by 10% within 15 years and is anticipated to save approximately 22,000 lives a year.
NHS trust bets on AI
One day after that announcement, University College London Hospitals (UCLH) and the Alan Turing Institute entered into a three-year research partnership to use AI to streamline hospital services and improve disease diagnosis.
UCLU will be the first National Health Service (NHS) hospital to try out AI in the UK, and the immediate goal of the partnership is to improve the flow of patients and staff through the hospital in a bid to both prioritise treatments for patients with serious conditions and shorten the waiting time in the Accident and Emergency department, which usually exceeds four hours across the country.
Tasks that are usually performed by doctors and nurses, including examining computed tomography (CT) scans, will be accomplished by AI as well. In the long term, the partnership is looking at applying AI and ML to assist doctors with the diagnosis and treatment of serious conditions such as cancers, as well as offering personalised care to patients.
In the US, Case Western Reserve University entered into collaboration with Microsoft’s Quantum team on 18 May to use quantum-computing-inspired algorithms to enhance its approach to detecting cancer. Case Western has developed an innovative technique called Magnetic Resonance Fingerprinting to improve the speed and accuracy of magnetic resonance imaging (MRI) tests. The collaboration will allow Case Western to harness Microsoft’s quantum-inspired algorithm to optimize the process.
Opportunities in pharma research
On the pharma side, drug companies have also explored how AI and ML can be used at all levels to accelerate drug research from the hunt for new compounds to looking for potential combination therapies, creating personalised medicine, and discovering new uses for previously tested compounds. They are also looking at ways that AI and ML can drive company growth.
Since the beginning of this year, a number of deals, partnerships and collaborations related to AI and ML have already been introduced by giant and small pharma companies with tech companies. Wave Life Sciences, a biotechnology company targeting genetic conditions with unmet need, entered into collaboration with Deep Genomics in April 2018 to implement MI in the search for novel treatments for genetic neuromuscular disorders. In March 2018, Mitsubishi Tanabe Pharma and Hitachi embarked on a new collaboration to optimise clinical trial planning with AI. In February 2018, AstraZeneca-Alibaba embarked on a partnership to implement technology including AI for patient diagnosis and treatment.
Roche’s acquisition of Flatiron Health, an oncology tech company, in February 2018, has provided the giant pharma company with access to patient data and clinical research-grade data in the oncology field, which is a central element to AI application in pharma industry. As both companies are oncology leaders in their respective fields, this $1.9bn acquisition will pave the way for the companies to advance data-driven personalized healthcare in cancer. Novartis’s partnership with McKinsey’s QuantumBlack in January 2018 offered a platform to analyse clinical trial operations with ML. In January 2018, Johnson & Johnson Innovation – a division of the big pharma company that focuses on accelerating early-stage science – announced a partnership with WinterLight Labs to predict dementia and neurodegenerative diseases from voice samples.
The use of AI and ML in healthcare is a relatively new application of the technology, and both are continuously proving to have potential in many areas of this industry. Although machines will never completely replace humans in healthcare, the combination of data, technology, and expertise will offer incredible possibilities and ample opportunities to healthcare and medical research, turning challenges of the past into solutions of the future.
With the advancement of technology, the healthcare industry will witness a new wave of innovation, as well as an increasing number of similar and highly anticipated deals, partnerships and collaborations that will hopefully boost the growth of the healthcare industry while offering high-quality innovations worldwide.
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