How to move from ideation to a scalable AI solution
On 16 March 2022, Deeper Insights founder and CEO Jack Hampson delivered the webinar Transforming healthcare using AI - How to move from ideation to a more scalable AI solution alongside guest speaker Darren Wilson from Smith & Nephew. This article is a summary of the topics covered.
If you work in healthcare, pharma or life sciences and are looking to get an AI project off the ground or scaled successfully, continue reading.
We're seeing more companies with AI in their roadmaps than ever before, in part due to increased access to open-source technologies and off-the-shelf solutions. As AI technology becomes more obtainable and affordable, a more democratised relationship is evolving.
We are delighted to be witnessing an increasing familiarity with AI and consequently an increase in trust, confidence and understanding of the value. The market is maturing from a speculative to a much more solutions-led approach. This focus on solving specific problems generates much greater value.
The value of AI for every stage of the healthcare journey is becoming more evident. In recent years, headlines heralding AI's contribution to drug discovery have become more commonplace. Additionally, AI is becoming more integral to healthcare in the clinical setting, improving the service of patient aftercare and predicting health outcomes.
As the role of AI across healthcare increases, so do the rules and regulations around it. The increasing interest and involvement of the FDA and other regulators helps build trust around AI's use in clinical settings - an important, progressive step for the industry.
In 2021 alone, AI technology M&A deals worth over £32.5bn were announced globally, up 31.5% year-on-year. (Source: Gartner)
Industry leaders are making visionary leaps in pushing the boundaries of what is possible with AI, from drug discovery to personalised medicine and aftercare support.
Learn more about some of the big breakthroughs:
Cloud computing has been a significant enabler for AI adoption and innovation, changing the way we host data and train models, and ultimately increasing the affordability and accessibility of AI for healthcare.
There have been huge breakthroughs in Deep Learning, Transfer Learning and Active Learning in recent years with a knock-on effect for managing unstructured data. We are now able to use datasets previously viewed as unusable (for example, videos and images) in a way that was previously unimaginable.
Thanks to Transfer Learning we can use smaller healthcare datasets, as well as adopting various methods to augment or synthesise data to generate larger data sets from smaller ones and use this information to train a model with greater accuracy and confidence.
With these advances, the healthcare industry has made great breakthroughs and the climate is prime to nurture further advancements in the coming years.
To get over the starting line there are a number of areas to consider:
Bear in mind the following to avoid a failure:
While AI is advancing rapidly there are not that many applications that have come to market as yet, but scaling is an important consideration from the offset.
Scaling considerations need to bear in mind IT infrastructure to deliver the AI pipeline in a working environment, which of course brings with it many challenges when concerning integration with hospital systems, and the associated data privacy and security concerns and constraints.
For AI-influenced surgery, for example, to determine the true reliability of the machine algorithms, stress testing needs to progress from cadaver environments to live surgery and ensure that practitioners are comfortable. Data can be collected fairly autonomously during a regular procedure.
Iterative input from all stakeholders and end-users is also crucial and making sure performance decisions are captured at every stage of the process, backed by trials and evidence.
Ongoing costs are a critical consideration to ensure you have a sustainable long-term solution in place.
To conclude, moving an AI project from ideation to a scalable solution takes a lot of planning.
We are reliant on data, and working against a checklist for data quality control and meeting regulations.
We need good communication channels across all stakeholders and be ready to adapt if the models are not training satisfactorily, if more data is required, or even if large scale changes are required to the entire project.
It takes a lot of hard work and persistence to take AI through the various gates of clinical trials and evidence the performance of the AI solution.
However, we believe the transformative power of artificial intelligence can solve the toughest human problems, and we’re excited to be part of it.