Patients need more effective treatment, and they need it now. In cancer treatment, faster, more effective therapy can translate into another shot at life. AI can now deliver this, but standing in between the tech and the patient is the massive roadblock of today’s sclerotic health delivery systems. Last week at CogX, we heard from experts who are getting round the blockage.
So - What’s going on?
In our earlier blog post we heard from Stefan Roever, using magnetic field therapy to shorten feedback loops and find out whether therapies are working. At CogX Stefan joined a panel of pioneers at the centre of the current healthcare evolution.
As a consultant radiologist Hugh Harvey, blends medical and technical knowledge to expose AI to medical imaging. Joanna Holbrook from Benevolent AI described how machines are speeding up treatment times and maximizing our data pools, while Fiona Nielsen of Repositive highlighted the real challenges of implementing AI.
Joanna Holbrook, director of Translational Biology at Benevolent AI, provided insight into how AI can maximise the efficacy of a drug by enabling both speed and targeted application. Currently initial drug development takes 10 years to reach clinics, meaning that data is outdated and a valuable clinical timeframe has been missed.
Companies like Benevolent AI utilise data at patient level, allowing treatment to be tailored to specific needs. With the increasing amount of data needed, machine learning will be essential to make sure health professionals stay on top of current practice.
"It will not be possible for any human -even the cleverest oncologist - to imbibe all that information. We need AI to help us."
Fiona Neilsen, is founder and CEO of Repositive which enables easy access to anonymised patient data. At Cogx she talked about radical changes within treatment approaches. By using mice as avatars for people, the cancer cells are placed under their skin in order to see which drugs are effective. This allows feedback in close to real time meaning treatment is personalised and highly effective.
As AI is rapidly changing the industry, companies like Repositive are making efficacy gains, sidelining the traditional pharma model. Most of the work is happening in the preclinical phase in smaller companies and then fed into bigger pharma companies.
Speaking from experience of creating a private equity-funded research company, Stefan Roever highlighted how the pharma business model is at risk of being broken. Software can spit out drug peptides that can be replicated anywhere in the world and are specific to the patient. However this creates a gap for pharma, as highly personalised treatment has no value for the pharmaceutical industry if it cannot be replicated and sold.
Faced with opposition from pharma and organisations like the FDA, Hugh Harvey Clinical Director at Kheiron Medical and Chair of the RCR Clinical Informatics Committee gave his unique perspective on the difficulties of implementing AI into the current healthcare systems.
“Currently we have no idea how to regulate deep learning techniques and regulatory bodies are more wary of AI-powered drugs.”
So Now What?
With AI developing at an exponential speed, a fast-scale re education is needed to successfully integrate AI models into healthcare. A re-education of a workforce is long-overdue, with so much of AI treatment now being based on algorithms, a new niche profession in healthcare is needed.
Harvey spoke of a new profession -The Algorithmist - being introduced into the healthcare system, to ensure that personalised treatment which is enabled by algorithms can be introduced at speed and at an affordable cost.
According to Harvey ‘algorithms are the new drugs.’ As treatment advances in all areas of health, technology has allowed us to treat in a tailored and patient centered approach. In previous blog posts we saw how innovation in preventative health has used tech to improve the healthcare system and patient experience.
Healthcare is one of the last industries to be touched by transformative tech. The speed of progress is breathtaking. The next innovation needs to focus on transforming hearts, minds and attitudes towards AI so that patients can reap the benefits this decade.