BL Medical Futurist Issue 1

Page 1

BL MedTech Society


Feature: The Future of Artificial Intelligence in Healthcare

Opinion: 3D Printing: The Past, Present and the Future

Contents feature 4

The Future of Artificial Intelligence in Healthcare

news 8

Wearable Doctors: Latest Developments in the Field of Portable Technology


Delivering Healthcare from Drone to Doorstep


opinion 13

Rise of the Robotic Surgical Revolution


Have We Solved the Blackbox Problem of Machine Learning?


3D Printing: The Past, Present and the Future


interviews 22

Katerina Spranger of Oxford Heartbeat


Bruce Hellman of uMotif


Pearse Keane on AI in Opthalmology


Lise Pape of Walk with Path

16 Editorial Team:


Matthieu Komorowski on Reinforcement Learning

Stephan Chee Manfredi D'Afflitto

Editor's Note Since time immemorial, the field of medicine and healthcare has been steadily advancing on an inexorable tide of innovation, driven by altruism and limited only by one’s own imagination. Perhaps it might be said that as an innovator, one only needs to ask one question – “why not?”. It does not take much for any person to realise that whilst it is easy to find themselves marvelling at the ingenuity behind every medical tool and procedure, there is still a plethora of unmet needs within every healthcare system. This is what innovation is all about – finding unique solutions for specific problems. It is about thinking beyond the bounds of current medical practice into what it could be in future where the problems of today no longer plague the societies of tomorrow. It is for this reason that the idea for this magazine was conceived. With this magazine, we hope to challenge current ideas within medicine about what is possible and what isn’t by exploring various ideas and developments within medtech. More importantly we hope that this would inspire our readers to think outside the curriculum in search of unique solutions to the problems they will encounter within healthcare. In this issue, there is a focus on artificial intelligence (AI) within healthcare as scientists and clinicians alike increasingly seek to optimise its potential, particularly that for diagnosis. However, is it really that simple and is that really all there is to it? From news on the latest medtech developments to interviews from experts in the field, we hope you enjoy this first issue.



Photo: DermEngine

Artificial intelligence (AI) was first described in 1956 by the computer WRITTEN BY

scientist John McCarthy in the first AI academic conference. AI is the term


utilised to describe a technology that is able to simulate human cognition,


intelligence and critical thinking. Since 1956, there has been extensive research in this area of computer science and its application in society. In the last years there has been a lot of fear regarding the replacement of various professions by machines, the majority of which utilise AI to some extent. Last year, Forbes Technology Council released a list of 13 jobs which will likely be automated by 2030, including customer service, delivery and banking services. The fear has nonetheless reached the medical sector with both patients and doctors worried about their medical consultations and jobs respectively. Should we be afraid of the integration of AI in medicine and how will this impact patients and doctors?



To the present day, AI is already used in the

of healthcare. In radiology, over the last decades

medical field with uses ranging from online

technology has improved the acquisition, storage

appointment scheduling and check-ins to drug

and reporting of radiological images. However,

dosing calculations and immunisation dates.

technology can still play a role in the future of this specialty mostly through the use of AI in diagnosing

"During a consultation, doctors spend approximately 53% of the time on their computer completing electronic healthcare records and ordering investigations." Within the consultation room, AI can revolutionise the current medical practice . The use of documentation technology such as dictation assistance and medical scribing technology allows the doctor to spend more time actually engaging with the patient. In this setting, AI is beneficial for the doctor, who spends less time on their computer and is more efficient, and for the patient

radiological images. AI has the ability to independently recognise complex patterns within images – compared to a human radiologist, AI is able to provide a quantitatively rather than qualitatively assessment of radiological characteristics. Furthermore, the efficiency of AI has been portrayed by a retrospective study published this year by Kim et al. which utilised data from South Korea, the UK and the USA; AI was found to be able to consistently detect cancer accurately in around 90% of cases compared to lower and more variable detection rates (50-74%) amongst experienced radiologists. Apart from its use in the detection and diagnosis of pathology, the technology could also be used for monitoring patients. For example, for patients on oncological screening programmes, AI would be able to provide accurate information on any

who has more direct contact with the doctor.

changes to tumour characteristics over time. In the

The potential of this technology in the medical

increase in the number of imaging tests performed

field is immense with applications in every aspect

NHS, over the last decade there has been a yearly thus resulting in an incremental strain on radiologists.

Photo: Kim et al, 2020, The Lancet Digital Health


"In 2018, the NHS reported an 18.3% increase in imaging activity from the previous 5 years."

Photo: MedTravelers


In this setting, the integration of AI may benefit

The Boston-based startup CompanionMX has also

both the radiologist, who will have an extra tool to

incorporated AI to develop an application that

help them with image interpretation in a more

analyses audio recordings of patients on treatment

time-efficient manner, and the patient, who may

for mental conditions such as depression and is

be able to receive more accurate results faster. The

able to deduce and monitor the patient’s mental

major question remains whether AI will eventually

health. The use of AI in chatbots is not restricted to

replace radiologists, but for the time being it

mental health, as observed by the English startup

appears that rather than replacing it, it will

Babylon which has been adopted by the NHS and

redefine the specialty. Looking at other specialties,

valued more than 2 billion dollars. The Babylon

AI has also been shown to be more effective than

chatbot helps patients to decide the best type of

dermatologists at diagnosing and classifying

care for their particular symptoms. The platform

malignant skin lesions.

does not provide a definite diagnosis; thus, it does not replace the usual role of the doctor. However,

With an ever-changing medical field due to the use

by providing the appropriate advice, it helps the

of novel technology and AI, it is essential that the

patient in getting access to the right type of care

medical education system integrates this new

and saves time and money for the NHS by

aspect of medicine. AI can be utilised as an

providing automated screening without the need

educational tool for medical students – for

of a doctor.

instance, the DXplain platform allows students to fill in the patient’s symptoms and investigations and it provides them with a range of potential differential diagnoses, with the available scientific evidence behind them, and the recommended actions to take. More importantly, it will be crucial to integrate the concepts of AI within the medical curriculum in order to prepare and educate future clinicians on this technology and its applications in healthcare. The growing prevalence of mental illness is a call for action to improve the accessibility and

Photo: The Drum

efficiency of mental health services - AI could lead

The potential application of AI in healthcare is vast

to a revolution in both the diagnosis and treatment

with several specialties that have already began to

of mental illnesses. Through the analysis of speech

integrate it in daily practice – the major benefits of

and written words, AI may be able to predict

AI are still to be explored. For the time being, it is

depression up to 3 months prior to the medical

difficult to predict the role of AI in healthcare in 20

diagnosis. There are already several applications

years and what changes this could bring for the

which have incorporated AI for this purpose such

patient along with medical training and practice.

as Ginger, which analyses the words a person uses and is able to provide the most appropriate counselling for the patient’s needs.




The Elvie Pump. Photo: Business Insider


Over the last few years we have seen many technological developments that are aimed at improving our health, however only some of them turned out to be specifically successful. Amongst the biggest areas of interest for investors nowadays are women’s health and sports science. These two hold a lot of potential for development as many life-changing solutions can easily be implemented with the right technological approach.

Impact on women’s health – the Elvie Pump In recent years, the industry of ‘FemTech’ - technology directed at women - has seen many diagnostic devices and applications that are specifically directed to improve women’s health. It is estimated that the market potential of such inventions is going to reach $50 billion by 2025 making it one of the significant areas of technological development. Most smartwatches offer apps synchronised with mobile devices which offer tracking of information regarding their reproductive health and family planning. Rising popularity of these interfaces leads Apple to introduce menstrual cycle tracking into their latest update for all devices.


Photo: News Beezer

One of the most important wearable devices is The Elvie Pump, a discrete breast pump that fits in women’s bras. It includes sensors that automatically stop pumping when the container is full and is connected with a phone app which allows for tracking the process. All the parts that require cleaning are dishwasher safe and make it easy for new mothers to continue with their daily routines while pumping milk. Previously, women had to rely on hands-on devices that were much less discreet and did not allow for performing other activities. A mother using the device for a long time said that ‘it gave me some of my freedom and a lot of my dignity back’. The design is, however, not flawless with women complaining about flashing lights that are visible through clothing and sound which is still noticeable in quiet spaces. Nevertheless, it is a big step towards empowerment of mothers in the working environment and an area which has a lot of potential for development.

The ECG in Apple Watch Series 4 Perhaps the most popular invention nowadays are smartwatches, particularly Apple Watches Series 4, since they were the ones to introduce a variety of health checks including: measuring the heart beat and taking an electrocardiogram (ECG), which both can be very helpful in diagnosing heart problems.

This apple technology uses an electrical heart sensor (single-lead ECG, similar to the ones doctors use) that collects results in your phone and can detect various life-threatening irregularities such as atrial fibrillation (irregular heartbeat) or very low and high heart rates. This year Apple published numerous stories about people whose lives were saved by alerts from their smartwatches. Dr Michael Spehr, a columnist with the German FAZ newspaper, explained that an Apple watch accurately diagnosed a patient’s previously undiagnosed atrial fibrillation by constantly reporting irregular heartbeat.The patient later wrote: “It’s true, the Watch contributes to prolonging my life”. However, this technology still shows rooms for improvement as it cannot detect heart attacks or other heart-related conditions. Another crucial contributing factor is the demographics of potential users of this technology as most of them are ages 25 to 34. In this age group atrial fibrillations remain a rather rare (1 - 2%) heart problem, so only by introducing this technology to people aged over 65 is there more potential for diagnosis.


Current Health Another interesting technology that has been developed in the USA is Current Health – the first AI Medical Monitoring Wearable Device that was approved by the FDA to monitor vital signs of patients home. The device measures pulse, respiration, oxygen saturation, temperature and mobility of patients and communicates with doctors via machine learning algorithms to detect any life-threatening situations. The wearable band is supplemented by a tablet that has education resources, medication reminders that aid especially older patients. This technology holds potential for preventing avoidable deaths and reduces the workload of nurses, carers and the primary care teams.


Photo: Los Angeles Times

Photo: BodyElectron

Sweat-sensor patches

Future Ideas

Scientists at UC Berkley reported a promising technology in the field of sport science: wearable sweat-sensor patches which can be used by athletes to monitor electrolyte and fluid levels in the body. These analyze perspiration under the patch, and are therefore also waterproof and can be used by professional swimmers. The device is connected with the users’ smartphone and processes information about sweat biochemical markers like pH as well as body temperature. It is particularly important for high-endurance athletes who tend to become very dehydrated during training and this technology is able to calculate the amount of water they should be consuming posttraining to become rehydrated.

The field of wearable technology is a rapidly expanding industry and more innovations are said to appear. For example, the next generation of Apple Watches are rumored to feature glucometers for diabetic patients and L’Oréal is designing UVA and UVB light-detecting sensors to measure radiation levels and make people aware of dangerous levels of sun exposure. There are many areas for development that hold a lot of potential for future investors in the medical field of wearable technology.



Photo: Zipline

Reports of unmanned drones delivering lethal

hospitals without valuable blood supplies which are

payloads to eliminate people and buildings is

in themselves logistically challenging given their

nothing new and much less spectacular now

short shelf-lives. Today, about 40 percent of blood

compared to when it was first employed as a

products delivered to hospitals in Rwanda is done

novel military weapon. In recent years however,

entirely by these drones.

what has conventionally been used to take lives is now being considered through the lens of altruism by the healthcare industry.

The entire delivery process starts with an order placed via email, text message or a phone call from the hospital to an operator at the distribution

In 2016, Jonathan Ledgard’s Zipline launched the first

centre. The operator then selects the correct blood

ever fully autonomous drone delivery network for

product from the various types stored at the centre

blood products in Rwanda. Traditionally, healthcare

and packages it into the loading bay of a drone. Its

products are transported by motor vehicles along the

wings are fitted, a new battery placed in, and the

many dirt roads in the country, which are vulnerable

drone positioned onto a catapult before being

to the heavy rains during the monsoon seasons that

launched into the air where it then flies along a

Rwanda experiences biannually. This can leave

pre-set route to its destination. Once directly over


its drop-off point, it releases its package which gently descends to the ground with the aid of a parachute and picked up by a hospital staff member. The drone then returns to the distribution centre where it is disassembled and stored until needed for another delivery. Following on with the project’s success in Rwanda, Zipline has since expanded its operations to Ghana and in 2019 trialled the delivery of vaccines, blood products and other life-saving medications to local hospitals by these drones. The company also continues to refine the drone’s design, with its latest model capable of delivering payloads of 1.75kg over distances up to 161km (100 miles). Meanwhile in Tanzania, DHL is working with the local authorities to utilize the vertical take-off and landing (VTOL) DHL Parcelcopter 4.0 to deliver medical supplies to hard-to-reach places. In 2018, the drone was successfully used to deliver medical supplies to Ukerewe District Hospital (UDH) in Nansio from the regional capital of Mwanza as part of a trial.

40 percent of blood products delivered to hospitals in Rwanda is done entirely

severely impedes the prompt delivery of emergency medical supplies,

by these

temperature-sensitive products like vaccines and test samples. In


contrast, the drone consistently traversed between the capital and UDH in 40 minutes. In more developed countries like the US, the logistics company UPS has launched a new revenue drone delivery system last year in North Carolina. The programme utilises the Matternet M2 Quadcopters to deliver up to 5lbs (over 2.3kg) of medical samples at a local hospital. Earlier, the company had also partnered with CyPhy Works to successfully deliver an asthma inhaler to a child on an island inaccessible by land vehicles as part of a trial in 2016.

could cut the

In Europe, a drone is currently being developed by Delft University of

average time

Technology to rapidly deliver an automatic external defibrillator (AED) where needed. Along with its in-built AED, the prototype contains a

taken to deliver

camera, microphone and speakers through which an operator can

an AED to a

communicate instructions to by-standers at the scene. Alec Momont, who leads the project, predicts that the drone could cut the average time taken to deliver an AED to a sufferer from 10 minutes to just 1 minute. It seems therefore that drone technology is set to change the entire face of medical logistics around the world in the coming decades. With some companies like Boeing, Sabrewing and Elroy currently developing drones capable of delivering large payloads over long distances, there is huge potential for remedying acute and chronic shortages in medical supplies worldwide, and closing the health inequality gap that much more.


"The drone

sufferer from 10 minutes to just 1 minute"

News: Delivering Healthcare from Drone to Doorstep

Albeit receiving regular supplies via its overland route, the 6 hour journey

"Today, about


There has been a rise in the prevalence of robotics and automation across all industrial sectors, and its introduction into that of the field of healthcare is not new. Indeed, healthcare has moved beyond the traditional format of simple treatment and surgical procedures, and has begun to experiment more and more with the arsenal of technological tools that have become available to doctors and surgeons alike over the past decade. As it stands, robotic surgery is slowly becoming increasingly common within the hospital setting, particularly within the fields of thoracic and cardiovascular surgery (e.g. as part of mitral valve repair, atrial septal defect closure, and multivessel minimally invasive coronary artery bypass grafting) as

well as general surgery. Surgical robotics are used due to their high level of precision and ability to execute fine and delicate manoeuvres that would otherwise not be possible using the dexterity of the human hand alone. Robotics has transformed the way and speed at which common surgical procedures are performed. Indeed, there are many benefits to be reaped from using these highly advanced technological tools. Regarding benefits to the patient, most robotics-based procedures are minimally invasive. This thereby enables smaller, more discrete incisions to be made, avoiding any chances of inadvertent punctures and providing better outcomes for the patients. Patients often also benefit from Photo: DesignNews


Opinion: Rise of the Robotic Surgical Revolution

faster recovery, enabling them to resume normal work more quickly, and to spend more quality time with their family and friends. Less pain and risk of major blood loss is also endured by the patient, reducing their dependency on unnecessary painkillers. Moreover, robotic surgery carries with it a lower risk of post-op infections compared to traditional surgery. In addition to the benefits to be reaped by the patient, there are many favourable aspects of using robotics from the perspective of the doctor and surgical team. For example, the use of robotics has been associated with better surgical performance and confidence. Surgeons may feel more comfortable utilising a robotics-based approach since such technologies provide precision and accuracy through the use of 3D cameras and hands that can be expertly and calmly controlled. Indeed, studies evaluating surgeon and surgical team perspectives on the implementation of robotics in the operating room have reported largely positive feedback, with surgeons having praised the ergonomics. Moreover, the imaging available also provides better depth perception. In addition, the

ability to use robotic instrumentation across a broad range of surgical specialities is a geatly attractive feature of its users. For example, surgical robotics may be used across a variety of surgical subspecialities such as gynaecology, colorectal and urological surgeries, ENT, orthopaedics, and neurosurgery, to facilitate the execution of high levels of surgical precision. Moreover, robotics provides a solution to the traditionally lengthy surgical procedure, which may induce strain, shoulder, and back pain in the unfortunate surgical trainee. Thus, robotics enables the procedure itself to be less physically demanding, since the entire operation can be performed from the comfort of a seat or console located elsewhere within the operating theatre. However, various limitations exist. For example, surgical robots bring with them a high cost of installation, are costly to maintain (e.g. the da Vinci Robot Surgical System), and their operation requires additional training which may also contribute to the overall expense. Moreover, technical complications and risks may include mechanical

Photo: Healthcare Market Experts

The Da Vinci Robot Surgical System


failures and malfunctions (albeit rare), sparks, electric arcing and burns which may inadvertently cause tissue damage, and the theoretical risk of nerve palsies due to the compression of vital nerves by the robotic arm. As a whole, there are various benefits to using robotics in the wonderful and delightful field of surgery. These attractive features have evidently led to their wide-spread adoption not only on a subspecialty scale, but on one that is also worldwide. In the future, robotics may be used for remote applications such as humanitarian relief. Alongside surgery, robotics may also one day provide laboratory assistance to facilitate the location and drawing of blood with less pain and anxiety for the patient, may carry bed linens and meals from floor to floor, may help to disinfect patient rooms and operating suites (thereby reducing the risk for infection transmission), and may even be utilised in the dispensing of daily medication. It is likely that robotics will prove a game changer to the way in which surgery and healthcare as a whole is practised.

Photo: Verywell Health

Have we solved the blackbox problem of machine learning? By Alex Deighton

Over the past decade the rise of artificial intelligence has been nothing short of meteoric. Healthcare, industry and big data companies have embraced this emerging technology with open arms and it is becoming an ever more prevalent part of our lives. Realising the full potential of this technology, however, has been fraught with difficulty. Media sensationalism and extravagant promises of a machine learning revolution have brought about repeated cycles of hype and disappointment, leading to the so-called ‘AI winter’ and threatened the collapse of the AI industry before it had even begun.

Fortunately for us, the AI winter is finally over and the AI spring has dawned. Publications on the topic of machine learning have sky-rocketed in recent years, whilst the global AI industry is expected to grow from a value of $4 billion in 2016 to an impressive $169 billion in 2025. This sudden boom has driven the creation of thousands of AI startups and pushed bigger corporations to branch out, with Microsoft, Google and IBM all opening healthcare divisions within the last couple of years. In doing so, this renewed interest has cast the spotlight on problems that have been frustrating AI researchers for decades.

Photo: CSCR

Opinion: Have We Solved the Blackbox Problem of Machine Learning

"AI can process vast quantities of data, predict behaviours and preempt health conditions, but it cannot tell us how, and this lack of transparency causes big problems when things go wrong."

One such challenge facing researchers is the black box problem of machine learning, the question of how rather than what. Being so used to feeding information into one side of a machine and getting results out the other, we often pay little attention to how the machine actually reached this solution and the same goes for artificial intelligence. Here this socalled ‘black box’ between inputs and outputs obscures the decision-making process and leaves researchers in the dark, preventing us from understanding how our algorithms think and leaving plenty of room for bias. Granted, this may be of little significance when predicting shopping trends or patterns of twitter use, but when AI is being used to diagnose cancers, or convict criminals, the question of how becomes rather more important. AI can process vast quantities of data, predict behaviours and preempt health conditions, but it cannot tell us how, and this lack of transparency causes big problems when things go wrong. To put this problem into perspective, take a world without this black box for a minute. A machine learning programme mistakenly diagnoses a healthy individual with cancer, but why? Without the black box, we can look at how the programme made this decision and what went wrong. Perhaps it was too reliant on external guidelines, perhaps it detected an artefact on X-ray as a malignant tumour. We can understand this error and make adjustments, but as it stands, this isn’t the case. The black box muddies the water. AI programmes are brilliant, but secretive, and this is an issue that needs solving before we can ever trust AI to make the big decisions we can’t.

Thankfully for us, some of the best and brightest have risen to the task. Google’s AI division, DeepMind, published an article early last year presenting their algorithms capable of diagnosing over 40 ophthalmic diseases from optical coherence tomography, or OTC scans. Here these algorithms were shown to match the world’s leading specialists and even make brand new connections, determining both age and gender from nothing more than retinal images. The results of the study were nothing if not impressive, but what was even more revolutionary was how Deep Mind addressed the black box problem which has plagued AI for decades. DeepMind’s new system, instead of training a single neural network, used two, compartmentalising the diagnostic process into two separate stages. Stage 1, segmentation, would identify visual structures, creating a tissue map, with labels for anatomy, pathology, and artefacts. Stage 2, classification, would then analyse these labels and diagnose or refer as appropriate. This process, reflecting a real world diagnostic pathway, allowed clinicians to inspect the AIs decision making, and is key to more safely integrating neural networks into current clinical practice. This way, researchers were given an explanation, rather than a simple diagnosis. Crucially, work didn’t stop there. This small step in making AI more transparent has been followed up with the creation of Google’s new facility, Explainable AI. Explainable AI aims to inform clients about the performance and potential shortcomings of their machine learning models, making the decision-making of artificial intelligence less mysterious, and a bit more trustworthy. Despite this, Google themselves


recognise that there is still a long way to go before artificial intelligence becomes fully transparent and unfortunately, despite these developments, the black box problem is likely to persist. Why? Because there are downsides to transparency too. If the world can figure out how your AI works, they can figure out how to make it without you and this can be expensive when you’ve poured millions of dollars into developing your system. Meanwhile, other big corporations made billions at a time when AI was unregulated, creating and selling systems that are both biased and unethical. Extricating biased black box AI from the world would probably push a number of companies out of business and result in hundreds of billions of dollars lost. As a result, as AI algorithms become increasingly transparent, many of these big businesses are lawyering up, invoking intellectual property laws to avoid giving details about how their algorithms arrive at important decisions. Sadly, we may be trading a technical black box for a legal one, and somehow this seems even more wrong.

Photo: SingularityHub



3D printing’s popularity has skyrocketed in

3D printing has a role to play in several

recent times amongst technology

industries such as dentistry, architecture,

enthusiasts. But why? Initially we were

aviation and lately in healthcare. There is

drawn in by its inherent coolness, but we are

currently 18% growth in the 3D printed medical

now beginning to see past that, realising just

device market per year and it is estimated that

how useful it can be. But what is it?

this market will reach a value of $3.5 billion by

3D printing is essentially an additive

implants and prosthetics which have been in

manufacturing process that involves placing

use since the early 2000s. In 2016 for the first

down thin layers of a material successively in

time 3D printing was used in the NHS when it

order to recreate a digital model in three

was combined with robotic surgery. It was

dimensions. It was once just a fantasy but with

pioneered at Guy’s and St Thomas’ where a

the technological revolution we are in, it has

cancerous prostate was modelled using MRI

become a reality with the potential to open

and then printed to help plan an intricate

many opportunities in many fields. Its

surgical removal. Surgeons have stated that

beginnings can be traced back to Charles Hull

‘having a 3D print of the anatomy gives an

in 1986 who patented stereolithography where

increased understanding of spatial orientation’.

acrylic polymers were layered and then

Studies have shown this to reduce operating

toughened with UV light – his first print being a

time, complications and need for

small eye wash cup.

reinterventions. To help surgery further, tools

2025. It began with the production of dental

such as screws and plates can also be custom printed to improve the precision of an implant.



These models can then also be used for the benefits of education to understand anatomy that can be difficult to visualise. As well as helping students, printing models can help patients understand and make informed decisions about surgery as opposed to having to interpret 2D images on CT or MRI scans

"So currently, 3D printing has made significant strides in creating prosthetics, implants, anatomical models and dental apparatus. But what is the future? Why are we so excited about this technology?"

The prime industry for 3D printing to crack is tissue engineering. Currently we isolate stem Photo: KLS Martin Group


cells, grow them in labs and then place them upon scaffolds to develop into tissue. This is a

The largest healthcare industry currently

haphazard and slow process. 3D printing has

benefitting from 3D printing is dental care.

the potential to accurately create a tissue

There is a diverse use of 3D printing here

structure via inkjet-based bioprinting in which

already – spreading from its use in producing

living cells or biomaterials can be ‘dropped’ in

transparent aligners to crowns and models of

layers to form a tissue. Stem cells are first

teeth for surgery. The prints are custom, sterile,

differentiated into the organ cell type of choice

complex and low cost. This is because it can be

and then bioink ‘reservoirs’ can be created to

run on a small scale to produce bespoke small

then print the tissue of choice. This concept is

implants and prosthetics using various

still being researched and so far we have been

materials. 3D printing is resulting in faster,

able to create prototypes of a knee menisci,

cheaper treatments. Practices can design a

heart valve and an artificial ear. Printing tissue

model and print it in the same room. In fact,

is a stepping-stone to what would be the

crowns can now be replaced in less than an

ultimate application – using 3D printers to

hour as a result of 3D printing compared to the

create complex organs such as the heart or

usual several weeks. Furthermore, these

liver. These are larger and require

models and designs can be shared worldwide

vascularisation and nervous supply which

and so people around the world can instantly

complicate it more, although institutions are

produce the same model from their own

starting to print functional capillaries which is a


sign of promise.



We have begun to print synthetic tissue for patients with burns and it is believed that bioprinting could potentially produce functional organs within the next 10-20 years. This would be a huge achievement in the field of medicine. Transplant waiting lists are ever increasing and the ability to bioprint organs can reduce that waiting list and provide many people with potentially lifesaving treatment. Along with its use in transplantation – 3D printed tissues have a role in research as drugs can be tested on these organs during development to attest their safety and efficacy. This can be further extended to using a patient’s stem cells, printing tissue using those stem cells and then testing the efficacy of a drug on that specific personal printed tissue. This brings a different dimension to the idea of personalised medicine. Another big step in 3D printing is its potential use in pharmacy. Currently, we are limited by how we can divide dosages – however with 3D printing, pharmacists could potentially print out and dispense a specific dosage of a medication to provide optimum treatment.

Polypharmacy is also becoming an issue as patients are having to take more and more medicines. 3D printing can be used to combine medications to print a single tablet to improve compliance and ultimately improve the management of chronic conditions. However, despite all this excitement about 3D printing we must be realistic about timescales. The goals of having 3D printed tissue and organs, or medications printed in the pharmacy are still just goals and we are still some way away from achieving this. We are not just limited by the technology but also factors such as healthcare regulations, copyright legislations, and even ethics. However, we are on that journey, and the 3D future looks bright.



In a few words, could you


Katerina is a Royal Academy

describe the technology behind

PreSize™ enables clinicians to better

of Engineering Fellow. She

Oxford Heartbeat and how it works?

visualise how a stent might behave

holds a PhD in Biomedical Engineering


When it comes to minimally invasive,

University of Oxford and has

high-risk surgeries, particularly those

previously research

from worked






Sony Science

Laboratory in Paris. Named a “founder striving to change

involving the insertion of stents into blood vessels in the brain, there is an astonishing




involved. Primarily, it’s always been

the world” by Forbes, and


“Young Innovator of the Year



anatomy before surgery. It’s equally



Conference Breakthroughs and Society.

on in

Walls Future Science


predict inside

the the

stent’s patient’s

tricky to decide exactly where the stent should be placed, because even the most minor differences in device dimensions can have a huge impact


when deployed and choose the best one for a particular patient case, bringing




surgical outcomes.

What do you believe are the benefits for surgeons of using Oxford Heartbeat’s software? The





surgeons are able to select the best stent for each patient, reducing the propensity for complications during and after the procedure. PreSize™

on the patient.


We were perturbed to find out that

stents available on the market by




20% of patients who undergo brain surgeries



complications arising from ill-fitting stents, among other reasons. Given that each stent costs around £10,000, a lot of waste is incurred when







presenting them with their digital equivalents ahead of the surgery. Secondly, PreSize™ facilitates safer surgeries by enabling surgeons to rehearse





surgeries aren’t successful.

making them better prepared and

These discoveries drove us to develop

surgery, it also helps to reduce the


PreSize™ Neurovascular, a medical device software that makes brain










particular stent in an individual







intra-operative radiation exposure under which these procedures are being performed.

Finally, PreSize™ has been shown to

For a long time now, I have been

simulate stent deployment with a

fascinated and awed by the most


crucial moments of surgical decision-




Interventional neuroradiologists

making. The realisation that I could

have emphasised that knowing the

apply my skills to improve these

tool is highly accurate and safe really

high-risk procedures, and make a

helps to reduce the uncertainties

difference to the lives of patients in

and risks involved in their decision

doing so, really changed my life. I

making processes. We want to play

have continued to find inspiration

our part in making difficult medical

and meaning in this idea as Oxford

procedures less anxiety-inducing for

Heartbeat expands.

everyone involved. I also encountered and tackled many

In your view, aside from minimally

matters for the first time: hiring,

invasive brain surgery, in what


other types of surgery do you think

regulatory approval, setting up a

Oxford Heartbeat could be utilised

clinical trial, and the list goes on. I’m


looking forward to many more “firsts”




as we continue to grow and explore

We’re really excited to be developing

new ways to make complex surgeries






analogous to PreSize™, which can be





surgical procedures on other parts of the body. Our aim is to support complex surgeries in any way we can.

Above all, I am grateful to work with my





appreciate all the support we have received from the government, the NHS and our network of friends in

What was your experience like of founding a start-up company in the






medical field? It has been the most exciting journey so far! I have stretched myself in every possible direction and am humbled to have learnt a lot along the way. My background in computer science and engineering showed me the power of technology to challenge the status quo. My academic



experiences have also helped me to cultivate a conviction that you are able to make the world better, when you see an opportunity and work hard towards it.




Bruce is the CEO and co-founder of uMotif, a platform which focuses on collecting patient data for clinical trials. In his early career, Bruce worked on clinical trials at British Biotech and he subsequently joined the UK Civil Service Fast Stream.

In a few words, how did you come up with the idea

is highly valuable for them. Once you have an idea

of uMotif?

for something that delivers value you need a great

Back in 2012 my co-founder and I met two people with Parkinson's disease. In speaking with those patients and understanding the challenges they faced in managing their condition, we realised that our simple idea for health tracking could potentially

it to customers and then deliver it excellently. The team is all-important, as it's often the case that you'll need to refine or adapt your offering, and a good team will be able to pivot with those changes.

help patients capture more data to support their

Is the COVID-19 pandemic fuelling more

healthcare journey.

conversations around virtual trials?

Can you briefly explain how uMotif works?

Yes, COVID-19 is creating a real sense of urgency to

Today, uMotif is a software platform used by

adopt virtual trials, both to help with vital research

pharmaceutical, academic, and health researchers to help gain new insights. Patients remain at the core of our approach and we focus on providing people with an engaging experience to capture their data. The data they capture is sent securely to the cloud,

into a vaccine or cure, as well as helping other research to continue. We are seeing delays to clinical trials due to sites closing and the social distancing measures currently in place. If some of these trials had a virtual methodology they could potentially be

where it is accessed by specific researchers for each

running without disruption.


For COVID-19 trials, virtual approaches can help






completing daily diaries, tracking symptoms, and


connecting wearable devices the data captured by

symptomatology and impacts of the virus. We’ll soon

patients helps researchers understand the efficacy of

be supporting a number of studies in this area and

a new drug, the impact of an existing drug on new

hoping to make an impact to bring the crisis to an

therapy areas, or understand how a disease can

end sooner.

progress. As a software platform we support multiple projects across the globe and in any therapeutic area. In your experience, what do you think are the most important components of a successful start-up? It's essential to focus on delivering value to a specific target market, who need the service/good/product the start-up offers. In our case - we help researchers undertake essential research studies and trials, which


team to create the service (or product) and package







Do you think the current COVID-19 pandemic will cause us to see more virtual trials in the future? Absolutely. The fact that coronavirus is so contagious and has caused countries the world over to close business






distancing, demonstrates the need for more virtual and hybrid trials. The world will be a changed place post Covid-19 and clinical trials will be too.



In a few words, could you explain how optical coherence tomography (OCT) imaging works and how it is utilised? Optical coherence tomography (OCT) is a form of imaging which was first invented in 1991. It is a bit like ultrasound imaging but measures the reflections of light waves rather than the echoes of sound waves. It achieves this using interferometry. As the wavelength of light is so much less than the wavelength of sound it produces very high resolution images. The axial resolution of OCT in the retina is approximately 5 microns, an order of magnitude better than imaging modalities such as CT or MRI. Even better OCT scanning is quick and safe and easy to do. It can be done on a 5 year old or a 95 year old!

Following your work with Google DeepMind Health, how is artificial intelligence able to interpret OCT images? Working with DeepMind we trained a deep learning system to analyse retinal OCT scans. The AI system can first delineate any pathology in the retina. It then makes both a diagnosis and a triage decision on the pathology. For example, in a patient with age-related macular degeneration (the commonest cause of blindness in the UK), it might make








recommend “Urgent” referral.

What do you think are the potential benefits of using artificial intelligence to interpret OCT scans? We hope this system will allow patients with the most severe, sightthreatening retinal diseases to get seen by ophthalmologists with specialist expertise as soon as possible. By allowing earlier detection and thus earlier treatment, we think this system has the potential to

Dr Pearse Keane is a Consultant Ophthalmologist at Moorfields Eye Hospital and an NIHR Clinician Scientist at the Institute of Ophthalmology, UCL. His research focuses on the application of new technologies, such as AI, virtual reality and advanced imaging, in the field of ophthalmology. His work in collaboration with Google Deepmind involved the use of machine learning to analyse optical coherence tomography (OCT) images.

save sight.

Do you believe artificial intelligence could be applied to other aspects of ophthalmology? AI is already being applied to many other areas of ophthalmology, including







Ophthalmology is very much at the forefront of the AI revolution in healthcare and I think it can act as an exemplar for other medical specialties. I’m also proud that Moorfields Eye Hospital, the NHS, and indeed the UK as a whole can be central to this revolution.




FOUNDER OF WALK WITH PATH LIMITED Lise is the Founder of Walk With Path which, since it was founded in 2015, has developed "Path Finder" - a visual cueing shoe attachment to aid patients with Parkinson's disease - and "Path Feel" - which is a haptic feedback shoe insole to help with balance.

In a few words, what are the major benefits of

Can you tell us more about the Path Feel

using Path Feel and Path Finder for patients with

technology and how you hope it could be used in

Parkinson’s disease?

the future?




Vibratory feedback from Path Feel acts to augment

with Parkinson's who are struggling with their gait

muscle stimulation, leading to improved balance.

and are suffering from Freezing of Gait and

Use of a vibratory insole has already shown in a

Shuffling. The laser light attachment provides a

group of healthy elderly people to improve balance

visual cue, which triggers the brain to take the next



vibrational feedback to the bottom of the foot can












addition balance






feedback to the soles of people with Parkinson's



who have balance issues and are at risk of falls.

neuropathic and stroke patients, among others.

The vibration applied to the feet of Parkinson's

With Path Feel Insole we aim to help several

disease patients has been found to be effective in

groups of vulnerable individuals with mobility,

improving gait patterns, and significantly improves

balance and gait issues on a large scale and for the


product to become a go-to tool in preventing PD from getting worse (by staying active).

How does the use of laser lights help to improve mobility and gait?

What do you think are the main applications of

In Parkinson’s disease, sensory cueing such as

design engineering in the medical field?

visual and auditory has been long proven to

Medical devices tend to be heavily engineered, to

improve gait (Lebold and Almeida, 2011). Two main

achieve the desired function, however, sometimes I

mechanisms have been suggested to underlie this

think we overlook that the persons who will be

phenomenon. It has been demonstrated that in

using them are ordinary people who also care

Parkinson’s patients, visual dependence is used to

about what they may look like or they care about



the desirability of a product in their home. I think

feedback. At the same time, attentional processing

that design engineering has a huge role to play in

is also used to alleviate automaticity in walking. It is

personalising products more and making them


desirable - to everyone.









dependence play a role in gait control (Azulay et al., 2006). Projection of a visual cue, through Path Finder, may allow the patient to by-pass deficits in their internal cueing system by augmenting both sensory and attentional abilities.


In your opinion, what are the major advantages for the NHS of introducing Path Feel and Path Finder? NHS introducing Path Finder would provide massive advantages to not only the individual suffering from PD, but also the personal carer, the nurse and the health care system. With our currently 300 users (around the world) - if these were all in the UK, we would estimate saving the UK health care system about 100 falls which amounts to costs of £648,738 in admissions to the hospital plus a potential £831,554.37 in surgery. The impact of Path Finder reaches far beyond the quality of life for the individual as with most individuals with Parkinson’s








healthcare professional helping them on a daily basis. This means that we have effectively changed the daily lives of 1200 people. This impact can reduce the amount of people accessing health care services, thus reducing the burden on healthcare professionals, and allow informal care-givers more time to engage in their community. In addition to this, some people with Parkinson’s







workforce due to Path Finder Laser Shoes. The above figures are based on 300 people. If the NHS were to introduce Path Finder Laser Shoes to a much larger percentage of the eligible population, the savings for the Health Care System would subsequently be much larger: in the UK the total PD population is 145,000. 50% of these are suffering from FoG and 65% of that subgroup would be eligible. If we estimate that 10% of the eligible population would use Path Finder due to the NHS introduction, it would affect 4,713 people. The costs saved the health care system is on average £4.934,31 per person (in admission to the hospital and for surgery), which amounts to saved costs of over £23 million for the UK health care system. And that is only 10% of the eligible population - there is a potential to save £230 million should Path Finder Laser Shoes become a standard treatment for PD patients. For Path Feel, we have less data as the product is not yet on the market, but we would estimate cost savings, efficiency rising, and improved quality of life for citizens - leading to less strains on the NHS. Long term, preventative measures could also become more common place.



In a few words, could you briefly explain what reinforcement learning is? I would argue that “AI” is mostly a buzzword, but what we actually do is machine learning. All machine learning deals with the overall same overarching goal: to generate structured information or inference from data. For example, in supervised learning, we learn the mapping between input data (e.g. patient vital signs on admission) and output data (e.g. their 28-day mortality) to build a predictive model (e.g. a mortality prediction score). Lots of different models allow to represent with more or less accuracy the relationship between input and output data, for example logistic regression, decision trees, or neural networks. Reinforcement learning represents a very different type of machine learning algorithms. Here, the goal is to optimise decisions to maximise some kind of reward. For example, the ground-breaking 2015 Nature paper by Mnih and colleagues described a reinforcement learning agent capable of playing Atari games with no prior knowledge of any game rules. Starting with a random strategy (called a policy in reinforcement learning), the virtual agent was able learn by trial-and-error to maximise the score and therefore to play most games with supra-human level of performance. Reinforcement

Dr Matthieu Komorowski is an

learning works particularly well for complex, sequential

intensive care physician and a

and stochastic (partially random) decisions. We can

former research fellow at the

apply the same framework to healthcare, where the

European Space Agency. He has

objective of physicians is to improve outcomes of our

attained his PhD at Imperial College

patients (the reward), similarly often facing complex sequential decisions, for example deciding every time we review a patient with suspected infection whether the time has come to initiate antibiotic therapy.

Laboratory for Computational

intelligence could be used in the treatment of

involving secondary analysis of

Machine learning algorithms can be classified into supervised,



reinforcement learning. These 3 types of algorithms


Physiology at MIT where he collaborates on numerous projects



with a particular focus on sepsis. He is also a visiting scholar at the

Can you tell us more about how artificial


London for his research on the use of machine learning in critical care,

healthcare records.

have different applications and objectives. In the

Following your work at the European Space

particular example of sepsis, supervised learning can

Agency, what do you think are the major

be used to predict sepsis before the onset, which in theory






administration. Unsupervised learning has been used to identify homogeneous subgroups of patients (also called phenotypes), which could have practical implications for their treatment or for the design of randomised controlled trials. Finally, reinforcement learning could be used in theory to optimise a wide range of sequential decisions, from weaning of mechanical ventilation, to dosing of insulin, heparin, intravenous fluids and vasopressors.

in space? This is another very exciting area for healthcare innovation. However, at the moment, the need to perform invasive medical procedures in space is very limited, because space flight is confined to low Earth orbit and pre-flight medical screening is extremely stringent. The contingency plan for any serious medical condition is to evacuate the International Space Station, which is possible at any moment using a Soyuz capsule. However, plans for exploration

In your opinion, what are the potential applications of artificial intelligence in critical care?

and/or colonisation of the Moon and Mars have been laid out and will push further the limits of human experience in austere environments. Science fiction

The potential applications of AI/machine learning in intensive







applications listed above, many AI models have been proposed to quantify severity of illness and predict mortality, help with the design of new antibiotics, predict antibiotic resistance directly from amplifying the genome of bacteria isolated in blood samples, or intelligent





weaning programme, to name a few. What I would like to emphasize is that the biggest hurdle is not the availability of algorithms, but the of











intelligent medical equipment capable of diagnosing diseases, performing autonomous surgery, provide empathy and psychological support, etc. The reality will most likely be much more stark and much closer to what remote crews experience in wilderness expeditions: having to deal with a huge range of medical









minimal If


specifically talk about anaesthesia, the challenges in


limitations in performing anaesthesia



something that we could term “the implementation gap”. Efforts should focus on assessing the tools in the clinical setting for safety and efficacy and putting them in the hands of clinicians to “close the loop”. Ultimately, evidence needs to come from randomised controlled trials comparing standard of care to doctors augmented by AI tools. Only a handful of

space are 3-fold: physiological (negative effects of the exposure to the space environment on the body, in particular the cardiovascular system), technical (lack of equipment and consumables) and skills (lack of onboard expertise and capability for real time telemedical support in the case of Mars). For these reasons, we have tested a range of simplified protocols and techniques in different environments, including during parabolic flights and a Mars mission simulation in the desert of Utah.

algorithms made it this far. As the field of “medical AI” develops, some have suggested that a new medical specialty might arise of clinicians specialised in data science and machine learning, best suited to navigate a learning healthcare system embedded with decision support tools built from AI algorithms. In such a system, medical decisions would not only be based on empirical evidence and RCTs, but on AI models capable of harnessing the data contained in large medical datasets, to deliver a more personalised medicine. Academic institutions now recognise this vision, for example through UKRI funded centres for doctoral training (CDT) in AI for healthcare. Imperial College London is the host of the AI4HEALTH CDT.


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