The AI tool, called Mia, was piloted alongside NHS clinicians in the UK and analysed the mammograms of over 10,000 women. 

Most of the participants were cancer-free, but the AI successfully flagged all of those with symptoms of breast cancer—as well as an additional 11 cases that the doctors failed to identify. Of the 10,889 women who participated in the trial, only 81 chose not to have their scans reviewed by the AI system.

The AI tool was trained on a dataset of over 6,000 previous breast cancer cases to learn the subtle patterns and imaging biomarkers associated with malignant tumours. When evaluated on the new cases, it correctly predicted the presence of cancer with 81.6 percent accuracy and correctly ruled it out 72.9 percent of the time.

Breast cancer is the most common cancer in women worldwide, with two million new cases diagnosed annually. While survival rates have improved with earlier detection and better treatments, many patients still experience severe side effects like lymphoedema after surgery and radiotherapy.

Researchers are now developing the AI system further to predict a patient’s risk of such side effects up to three years after treatment. This could allow doctors to personalise care with alternative treatments or additional supportive measures for high-risk patients.

The research team plans to enrol 780 breast cancer patients in a clinical trial called Pre-Act to prospectively validate the AI risk prediction model over a two-year follow-up period. The long-term goal is an AI system that can comprehensively evaluate a patient’s prognosis and treatment needs.

(Photo by Angiola Harry)

See also: NVIDIA unveils Blackwell architecture to power next GenAI wave 

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This collaboration will capitalise on Absci’s Integrated Drug Creation platform—seamlessly integrating with AstraZeneca’s expertise in oncology, aiming to expedite the discovery of a potentially game-changing cancer therapy.

Under the agreement, Absci will deploy its pioneering generative AI technology to craft a therapeutic candidate antibody tailored for a specific oncology target. The collaboration encompasses an upfront commitment, substantial R&D funding, milestone payments, and royalties on future product sales.

Sean McClain, Founder & CEO of Absci, said: “AstraZeneca is a leader in developing novel treatments in oncology, and we are excited to collaborate with them to design a therapeutic candidate antibody with the potential to improve the lives of cancer patients.”

Absci’s Integrated Drug Creation platform combines generative AI and scalable wet-lab technologies, generating proprietary data by scrutinising millions of protein-protein interactions. This data fuels Absci’s proprietary AI models, facilitating the design of antibodies that are later validated through wet-lab experiments.

This accelerated approach, completing the entire cycle within approximately six weeks, enhances the probability of successful development outcomes for biologic drug candidates.

Puja Sapra, PhD, SVP of Biologics Engineering & Oncology Targeted Delivery at AstraZeneca, commented: “This collaboration is an exciting opportunity to utilise Absci’s de novo AI antibody creation platform to design a potential new antibody therapy in oncology.”

The announcement follows Absci’s recent publication on the design and validation of de novo antibodies using their state-of-the-art ‘zero-shot’ generative AI model.

The collaboration between Absci and AstraZeneca should further help to demonstrate how AI can be used to revolutionise drug discovery.

(Photo by National Cancer Institute on Unsplash)

See also: AI & Big Data Expo: AI’s impact on decision-making in marketing

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LabGenius is currently focused on discovering new treatments for cancer and inflammatory diseases. The firm combines AI, lab automation, and synthetic biology for its potentially life-saving work.

Until now, the company has been using traditional GPUs for its workloads. LabGenius reports that switching to Graphcore’s IPUs in cloud instances from Cirrascale Cloud Services enabled its training of models to be reduced from one month to around two weeks.

“Previously we used GPUs and it took us about a month to have a functioning model of all the proteins that are out there,” said Dr Katya Putintseva, a Machine Learning Advisor to LabGenius.

“With Graphcore, we reduced the turnaround time to about two weeks, so we can experiment much more rapidly and we can see the results quicker.”

Specifically, LabGenius is using IPUs from Bristol, UK-based Graphcore to train a BERT Transformer model on a large data set of known proteins to predict masked amino acids. This, the company says, enables the model to effectively learn the basic biophysics of proteins.

“[The system] is looking across different features we could change about the molecule — from point mutations of simpler constructs to the overall composition and topology of multi-module proteins,” explained Tom Ashworth, Head of Technology at LabGenius.

“It’s making suggestions about what to design next… to learn about a change in the input and how that maps to a change in the output.”

One in two people now develop cancer in their lifetime. Current treatments often cause much suffering themselves and, while survival rates for most forms are increasing, only around 50 percent survive for ten years or more.

AI will help to find new cancer treatments that cause less suffering and greatly increase the odds of long-term survivability. However, while discovering new cancer treatments is the current focus of LabGenius, the company notes how the principles can be applied more widely to find new treatments for other horrible diseases that plague mankind.

“Graphcore has changed what we’re able to do, accelerating our model training time from weeks to days,” adds Ashworth.

“For our data scientists, that’s really transformative. They can move much more at the speed they think.”

(Photo by National Cancer Institute on Unsplash)

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Cancer is estimated to have caused 9.6 million deaths in 2018 alone. Around one in two will develop cancer in their lifetimes, and few will not know someone impacted by its blight.

Fortunately, advancements are making the discovery and treatment of cancer faster and more effective. IBM has been working on three AI-powered projects to help tackle this global challenge.

“Our goal is to deepen our understanding of cancer to equip industries and academia with the knowledge that could potentially one day help fuel new treatments and therapies,” IBM says.

The first project, PaccMann, aims to use deep learning for helping to predict the efficiency of cancer-fighting drugs. Hundreds of millions of dollars can be spent on getting approval for a single drug so identifying compounds likely to be most effective is paramount.

The second tool, INtERAcT (Interaction Network infErence from vectoR representATions of words), is a tool for automating the extraction of knowledge from scientific publications. It’s a tool anyone who spends time dissecting papers will understand, but few have such importance as seeking the information to help treat cancer.

“A particular strength of INtERAcT is its capability to infer interactions in the context of a specific disease,” IBM says. “The comparison with the normal interactions in healthy tissue may potentially help to obtain insight into the disease mechanisms.”

Last but not least is PIMKL, aiming to help predict the progression of a disease. Such a prediction helps clinicians to better personalise and design effective cancer treatments.

A web version of PaccMann is available here, or an open-source version here. The web version of INtERAct is here, or the open-source version here. Finally, the web version of PIMKL can be found here, or the open-source repo here.

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Generally speaking, it was much more grim to receive a cancer diagnosis roughly 50 years ago than it is today. The 5-year survival rate for childhood leukemia was just 14 percent from 1960 to 1963, for example, but has more than quadrupled to 61.4 percent. There has been a number of significant advancements in the world of cancer care and more breakthroughs have been made since the start of the 21st century than any period of time prior.

In the world of healthcare, and more specifically cancer care, technology is being used in new and exciting ways. Several studies have been done in order to test the lengths to which we can take our care with artificial intelligence to aid in the fight against cancer – and hopefully eradicate it once and for all.

Although there has certainly been progress and outcomes look better overall, cancer rates continue to rise. There has been an estimated 18.1 million new cancer cases and 9.6 million cancer deaths in 2018, according to a recent report, which is a rapid progression compared to the 14.1 million cases and 8.2 million deaths reported in 2012. This places an immense amount of pressure on oncologists and researchers to get ahead, however, artificial intelligence may be a reputable solution to help relieve this burden.

In particular, there is a rising rate of people with occupational cancers due to the shift in working conditions of the 20th century. People who worked in factories, on shipyards or in close proximity to homes or buildings constructed with asbestos-containing materials (ACM) are at-risk of developing rarer cancers, including the often fatal mesothelioma.

A cancer that develops in the mesothelium of the lungs, heart or abdomen following asbestos exposure, mesothelioma has a high rate of misdiagnosis. Its symptoms mimic common illnesses, including asthma, the pneumonia and Irritable Bowel Syndrome (IBS), which often leaves patients undiagnosed for decades. The incidence rate of mesothelioma has risen in recent years due to the 10-50 year latency period and many people who were exposed to the toxin at their workplace during the 1900s are now at risk.

As we continue into the 21st century, artificial intelligence could make a major impact for patients with mesothelioma because of the cancer’s especially poor prognosis. The life expectancy of a mesothelioma patient is less than two years on average, so early detection and diagnosis could be lifesaving. Fortunately, a few studies are already being done with regard to malignant mesothelioma and artificial intelligence, despite the cancer’s rarity.

In 2015, a study in Turkey successfully diagnosed mesothelioma with machine learning, a subset of artificial intelligence that involves a machine’s ability to perform a function without being programmed to do so. Through the use of two probabilistic neural network (PNN) structures, the disease was successfully diagnosed with 96.3 percent accuracy. Going forward, it will be interesting to see the additional studies done and progress made with regard to mesothelioma, as well as other occupational cancers, through the use of modern technology.

Artificial intelligence is not the only type of technology positively changing the outlook for cancer patients. Electronic health records (EHR), telemedicine and digital assistants, just to name a few, are providing doctors and patients with new routes for treatment. Researchers are also advancing age-old treatments, including radiology, to new levels as a means of better serving patients.

While the future of cancer is uncertain, but there is hope like never before. Cancer care is more specialized now than ever before and new studies are constantly being done to take our advancements even further. There’s no telling where we’ll be 50 years from now.

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The AI learns from historical patient data to determine what the lowest doses and frequencies of medication delivered the desired results to shrink tumours.

In some cases, the monthly administration of doses was reduced to just twice per year while achieving the same goal. Based on a trial of fifty patients, treatments were reduced to between a quarter and half of the prior doses.

Pratik Shah, Principal Investigator at MIT Media Lab, says:

“We kept the goal, where we have to help patients by reducing tumour sizes but, at the same time, we want to make sure the quality of life — the dosing toxicity — doesn’t lead to overwhelming sickness and harmful side effects.”

Some of the side effects of cancer medication can do more harm than good to a patient’s quality of life. By implementing the AI’s treatment strategy, the least toxic doses can be used.

The current model focuses on glioblastoma treatment.

Glioblastoma is the most aggressive form of brain cancer, although it can also be found in the spinal cord. It’s more commonly found in older adults but can impact any age.

Sufferers are often given a life expectancy of up to five years. Doctors often administer the maximum safe dosages to shrink tumours as much as possible, but with side effects that can impact a patient’s quality of life over that period.

In a press release, MIT said:

“The researchers’ model, at each action, has the flexibility to find a dose that doesn’t necessarily solely maximize tumour reduction, but that strikes a perfect balance between maximum tumour reduction and low toxicity.”

“This technique has various medical and clinical trial applications, where actions for treating patients must be regulated to prevent harmful side effects.”

Reinforced learning was used for the model whereby the AI seeks ‘rewards’ and wants to avoid ‘penalties’ so it optimises all of its actions.

The model started by determining whether to administer or withhold a dose. If administered, whether a full dose or just a portion is necessary.

A second clinical model is pinged each time an action is taken in order to predict the effect on the tumour.

In order to prevent just giving frequent maximum dosages each time – the researchers’ AI received a penalty whenever it handed out full doses, or a medication too often.

Without the penalty in place, the results were very similar to a treatment regime created by humans. With the penalties, the frequency and potency of the doses were significantly reduced.

The full research paper can be found here (PDF)

What are your thoughts on using AI to improve cancer patients’ quality of life? Let us know in the comments.

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How long it takes for radiation therapy plans to be created today can take days. Individual maps need to be created for each patient to determine where tumours need to be targeted.

This lengthy process is frustrating for the patient, their loved ones, and medical professionals who’d love nothing more than to spend time saving lives instead of creating plans.

Engineering researcher Aaron Babier and his team have stepped-in with AI-based software to automate the process and cut down how long it takes for a radiation therapy plan to be created from days to hours, potentially even minutes.

The team – from the University of Toronto’s Department of Mechanical & Industrial Engineering – also includes Justin Boutilier, Professor Timothy Chan, and Professor Andrea McNiven. Each of the researchers sees radiation therapy design as an optimisation problem.

By analysing historical radiation therapy data, the AI behind the software applied it to an optimisation engine to develop treatment plans. When the plans their software tool created was compared with those manually created for 217 patients treated for throat cancer, they were almost indistinguishable.

The difference, however, is their AI-powered tool created the plans within 20 minutes.

Babier explained:

“Right now treatment planners have this big time sink. If we can intelligently burn this time sink, they’ll be able to focus on other aspects of treatment.

The idea of having automation and streamlining jobs will help make health-care costs more efficient. I think it’ll really help to ensure high-quality care.”

Most of us have some unwelcome connection to cancer. According to statistics, one in two people in the UK born after 1960 will be diagnosed with some form of cancer during their lifetime.

Babier has a personal vendetta against the disease. He shares when he was 12 years old his stepmom sadly passed away from a brain tumour.

“I think it’s something that’s always been at the back of my head. I know what I want to do, and that’s to improve cancer treatment,” he says. “I have a family connection to it.”

What are your thoughts on the use of AI in cancer treatment? Let us know in the comments.

 Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London and Amsterdam to learn more. Co-located with the  IoT Tech Expo, Blockchain Expo and Cyber Security & Cloud Expo so you can explore the future of enterprise technology in one place.

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Cancer has a higher successful treatment rate the earlier it’s diagnosed. The later the diagnosis, the greater the risk of death or long-term debilitating effects.

In her speech, Mrs May will say:

“Late diagnosis of otherwise treatable illnesses is one of the biggest causes of avoidable deaths.

The development of smart technologies to analyse great quantities of data quickly, and with a higher degree of accuracy than is possible by human beings, opens up a whole new field of medical research and gives us a new weapon in our armoury in the fight against disease.

Achieving this mission will not only save thousands of lives, it will incubate a whole new industry around AI-in-healthcare. It will create high-skilled science jobs across the country – drawing on existing centres of excellence in places like Edinburgh, Oxford, and Leeds – and help to grow new ones.”

At least 50,000 people a year suffering from lung, prostate, ovarian, or bowel cancer will be diagnosed earlier due to AI, May will claim.

To achieve this goal, researchers will require access to large amounts of medical records to cross-reference patients’ lifestyles, genetics, and prior conditions to highlight when individuals are most at risk.

The UK’s National Health Service (NHS) has vast amounts of data. Every time a patient visits a service anywhere in the country, a record is made.

A patient’s medical record can include:

• • treatments received or ongoing

• • information about allergies

• • current medication(s)

• • any reactions to medications in the past

• • any known long-term conditions, such as diabetes or asthma

• • medical test results such as blood tests, allergy tests, and other screenings

• • any clinically relevant lifestyle information, such as smoking, alcohol or weight

• • personal data, such as age, name, and address

• • consultation notes, which a doctor takes during an appointment

• • hospital admission records, including the reason

• • hospital discharge records, which will include the results of treatment and whether any follow-up appointments or care are required

• • X-rays

• photographs and image slides, such as MRI scans or CT scans

How this data is shared and used to improve medical care remains a controversial topic. For example, the NHS’ sharing of data with Google-owned DeepMind has often come under scrutiny.

An independent panel last year found the deal between DeepMind and the Royal Free NHS Foundation Trust to develop an app for diagnosing kidney disease was ‘illegal’ and did not do enough to safeguard patient data.

Theresa May’s party, the Conservatives, have also faced widespread criticism over under-funding and privatisation of the NHS — leading to increased staff pressure and longer waiting times for patients.

Two-thirds of NHS trusts reported having at least one cancer patient waiting more than six months last year, while almost seven in 10 (69%) trusts said they had a worse longest wait than in 2010. One cancer patient waited 541 days for treatment.

If employed correctly, the automation offered by AI has the potential to greatly reduce staff pressure and improve patient care.

“Earlier detection and diagnosis could fundamentally transform outcomes for people with cancer, as well as saving the NHS money,” comments Sir Harpal Kumar, CEO of Cancer Research. “Advances in detection technologies depend on the intelligent use of data and have the potential to save hundreds of thousands of lives every year.”

“We need to ensure we have the right infrastructure, embedded in our health system, to make this possible.”

What are your thoughts on the use of AI in healthcare? Let us know in the comments.

 Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London and Amsterdam to learn more. Co-located with the  IoT Tech Expo, Blockchain Expo and Cyber Security & Cloud Expo so you can explore the future of enterprise technology in one place.

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The company was founded in 2011 by academics from Stanford University and Switzerland. SOPHiA provides software to analyse genomic profiles and diagnose patients to over 330 hospitals in more than 50 countries.

“Since inception, our vision has been to develop innovative technological solutions to help patients, equally wherever they live,” says Dr. Jurgi Camblong, CEO and Co-Founder of SOPHiA GENETICS. “SOPHiA acts as a real disruptor by breaking down the information silos in healthcare, meaning that the information from a patient in London or Paris can, for instance, help better diagnose and treat a patient in Lagos or Rio.”

Using AI to diagnose cancer

Among its recent advancements is an expansion into ‘liquid biopsies’ which uses an AI-powered app to help diagnose cancer. This is achieved by analysing DNA and tumour cells in urine, blood, cerebral spinal fluid, and other liquid samples.

“SOPHiA GENETICS is a company at the forefront of two rapidly changing technologies: genomic medicine and artificial intelligence,” explains James Wise, Partner at Balderton Capital. “By giving every healthcare professional a standardized, straightforward and fast way to analyze these complex data sets is an essential step to unlock the potential of data-driven Medicine.”

With a number of hospitals already onboard, and more joining, SOPHiA claims to have built the largest clinical genomics community. This enables the hundreds of institutions in the network to safely and anonymously share their findings and knowledge while ensuring patient data privacy.

SOPHiA envisions a near-future where each medical interaction starts with a genomic test. Corroborating this data with other patients’ health records will allow for earlier and more accurate diagnosis of issues while providing efficient personalised medicine.

The company was recognised by MIT Technology Review in its ’50 Smartest Companies’ who ranked it 30th. Existing investors include British tech star Mike Lynch’s Invoke Capital, and Alychlo, the investment firm of Belgian pharmaceutical entrepreneur Marc Coucke.

You can find more of our AI healthcare coverage here.

What are your thoughts on the use of AI for data-driven medicine? Let us know in the comments.

 Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London and Amsterdam to learn more. Co-located with the  IoT Tech Expo, Blockchain Expo and Cyber Security & Cloud Expo so you can explore the future of enterprise technology in one place.

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