By Dr Christophe Bianchi, Chief Technologist HealthCare, Ansys
The covid pandemic touched almost every aspect of our lives and its legacy can still be seen throughout society and the economy. During that time there was a laser-pointed focus placed on the healthcare industry – leading doctors and scientists instantly became household names as the fragility of life came to the fore of our lives. We turned to companies that developed vaccines, institutions that provided medical guidance and the media who provided us daily updates on the status of the virus.
Now, as the world moves on, the future of healthcare has retained its place under the spotlight. How can innovations and advancements help to make us more robust as a species and continue to improve bettering lives and fighting disease. Increasingly, the industry is turning to artificial intelligence (AI). The industry is learning to harness AI’s game-changing ability to improve the accuracy and efficiency of the simulation models that are increasingly shouldering the burden of decision-making processes used throughout the medicinal world.
As AI and simulation continue to develop and improve, the possibilities that are offered to the healthcare industry far exceed anything that could have been dreamt of in previous generations.
A gamechanger: In-silico medicine
When developing a new product, drug or process, rigorous testing is required to approve its use throughout the population. Traditionally this is done through bench testing or widely-criticised animal testing, followed by thorough clinical trials. This is often a very expensive and time-consuming process due to the number of inherent challenges that applying one standard cure to huge variations in people causes. New drugs for example can take as many as 10 to 15 years to bring to market by the time they are approved and licensed. In this process many drug and product candidates fail in their trials due to a lack of safety and efficacy, significantly contributing to time and cost.
Moreover, the lack of certainty at these early development stages, of how a product will perform or how the body will react, carries the underlying threat of serious damage that can be done to any patient or animal taking part in the study – clearly not only a practical but an obvious and profound moral issue.
Supported by AI and machine learning, in-silico medicine leverages computer modelling to revolutionise product development. It uses vast amounts of data to generate highly accurate computational models of patients that can then be used to simulate almost any medical outcome. This in-silico approach is able to be run on an almost limitless number of virtual subjects, as opposed to being limited to a relatively small number of physical subjects like animals or humans, which allows for much greater accuracy. Such methods benefit the industry with sharper understanding of how a disease progresses, more accurate prediction of the outcome of surgeries, or fast and effective evaluation of a drug treatment’s efficacy.
The impact of these innovative methods on the time and cost it takes to develop a new healthcare product is enormous. According to BIS research, thanks to in-silico drug discovery, the cost of bringing a drug to market fell from £2 billion in 2010 to £880 million in 2020.
However, the application of AI and simulation within healthcare is far-reaching and not just limited to product development. Digital twins are also having a game-changing effect in other areas of the industry.
Creating digital twins of just about anything
A digital twin is a highly accurate virtual replica of a real-life object, component or system. Sensors on the physical asset collect data that is then sent to its digital counterpart that give engineers real-time and accurate information on how it will react to any given set of circumstances. Implementations of digital twins across all industries are set to increase by 36% on average over the next five years. Globally, the market for digital twins is expected to grow from $6.9 billion currently to $73.5 billion in 2027. They have many applications, across almost every industry, but their role in healthcare is becoming increasingly implemented with great effect from virtual organs to planning surgery.
Reducing the risk of surgery
Surgery is increasingly implementing simulation-based digital twins – one example is that of endovascular aneurysm repair (EVAR). Every year, aortic aneurysms, when the aorta ruptures and causes massive internal bleeding, claim the lives of thousands of people. EVAR is a technique for repairing the aorta in order to eliminate an aneurysm before it can rupture and cause often fatal damage. Surgeons are able to make an incision in the groin area and insert a stent graph which they thread up the body until it reaches its target. Due to the nature of this procedure, it is fraught with danger. However, using simulation, supported by AI, a digital twin of the patient’s vascular system can be created. This then facilitates, to a highly accurate degree, the surgeons to practice ahead of time by mapping out exactly which route to take and how to manoeuvre through the body, all done virtually, with no risk to the patient. This technique has been used to great effect by Professor Jean-Philippe Verhoye who performs more than 100 successful EVAR surgeries every year. He believes it will become standard practice throughout the surgical world in as little as 10 to 15 years.
Progress within the healthcare industry has always been expensive, time-consuming and full of moral and practical obstacles. AI and simulation and their increased implementation are bringing scores of opportunities to develop new products, methods and processes in a way that was inconceivable previously. The pandemic brought our vulnerability as a species into sharp focus. Therefore, it is crucial that healthcare continues to advance in order for us to meet any future challenges. AI and simulation, and its continued adoption, will play a pivotal role in that success.