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If you want to imagine the future of healthcare, you can do no better than to read cardiologist and bestselling author Eric Topols trilogy on the subject: The Creative Destruction of Medicine, The Patient Will See You Now, and Deep Medicine.

Deep Medicine bears a paradoxical subtitle: How Artificial Intelligence Can Make Healthcare Human Again. The book describes the growing interaction of human and machine brains. Topol envisions a symbiosis, with people and machines working together to assist patients in ways that neither can do alone. In the process, healthcare providers will shed some of the mind-numbing rote tasks they endure today, giving them more time to focus on patients.

I recorded an interview with Topol in which we discuss his books. The podcast is titled Healthcares Reluctant Revolution because one of Topols themes is that healthcare is moving too slowly to integrate AI and machine learning (ML) into medicinea sluggishness that diminishes the quality and quantity of available care.

The first of Topols books, Creative Destruction, described how technology would transform medicine by digitizing data on individual human beings in great detail. In The Patient Will See You Now, he explored how this digital revolution can allow patients to take greater control over their own health and their own care. With this democratization of care, medicines ancient paternalism could fade. (In 2017, Topol and I co-authored an essay on Anatomy and Atrophy of Medical Paternalism.)

Deep Medicine is qualitatively different from the other two books. It has an almost-mystical quality. Intelligent machines engaging in AI and ML arrive at information in ways even their programmers can barely comprehend, if at all. Topol gives a striking example.

Take retinal scans of a large number of peoplethe sort of scans that your optometrist or ophthalmologist takes. Now, show the scans to the top ophthalmologists in the world and ask for each scan, Is this person a man or a woman? The doctors will answer correctly approximately 50 percent of the time. In other words, they have no idea and could do just as well by tossing a coin. Now, run those same scans through a deep neural network (a type of AI/ML system). The machine will answer correctly around 97 percent of the timefor no known reason.

Topol explains how such technologies can improve care. Today, radiologists spend their days intuitively searching for patterns in x-rays, CT scans, and MRIs. In the future, much of the pattern-searching will be automated (and more accurate), and radiologists (who seldom interact with patients today) will have much greater contact with patients.

Today, dermatologists are relatively few in number, so much of the earlier stages of skin care are done by primary care physicians, who have less ability to determine, say, whether a mole is potentially cancerous. The result can be misdiagnosis, delayed diagnosis, and the unnecessary use of dermatologists time. In the future, primary care doctors will likely screen patients using smart diagnostic tools, thereby wasting less of patients and dermatologists time and diagnosing more accurately.

In Deep Medicine, Topol tells the story of a newborn experiencing seizures that could lead to brain damage or death. Routine diagnostics and medications werent helping. Then, a blood sample was sent to a genomics institute that combed through a vast amount of data in a short time and identified a rare genetic disorder thats treatable through dietary restrictions and vitamins. The child went home, seizure-free, in 36 hours.

Unfortunately, healthcares adoption of such technologies is unduly slow. In our conversation, Topol noted that we have around 150 medical schools, some quite new, and yet they dont have any AI or genomics essentially in their curriculum.

Topol lists some hopes that observers invest in AI: Machines outperforming doctors at all tasks, diagnosing the undiagnosable, treating the untreatable, seeing the unseeable on scans, predicting the unpredictable, classifying the unclassifiable, eliminating workflow inefficiencies, eliminating patient harm, curing cancer, and more.

A realistic sort of optimist, Topol writes: Over time, AI will help propel us toward each of these objectives, but its going to be a marathon without a finish line.

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Artificial Intelligence and the Humanization of Medicine InsideSources - InsideSources

Introduction by Croakey: As with the COVID-19 pandemic, health authorities usually identify epidemics through public health surveillance, but could we do it earlier by being able to mine the vast un-curated public data available to us in this digital age?

Thats the hope and challenge from leading epidemiologist, Professor Raina MacIntyre, who heads the Biosecurity Program at the Kirby Institute, and Arunn Jegan, Advocacy Coordinator at Mdecins Sans Frontires (MSF).

They write below on the potential for harnessing artificial intelligence and the proliferation of the internet and social media for early detection of epidemics, saying that a signal for unusual pneumonia in China could have been detected in November 2019 and that CSIRO research showed that the Ebola epidemic in West Africa could have been detected three months before the World Health Organization was aware of it.

They ask:

Imagine if the COVID-19 pandemic had been detected well before it spread around the world, when there was only a handful of cases contained within a small geographic location?

Readers may also be interested in the series of articles published by the Croakey Conference News Service from the recent World Congress of Epidemiology, with one on innovative Victorian disease surveillance measures and their critical role in the states COVID response to be published soon.

The SARS-COV2 (COVID-19) pandemic has caused devastation around the world, and even in vaccinated populations, it continues to mutate into dangerous variants of concern. With the onset of the Delta variant, we assume the death toll will rise beyond five million in 2021.

This is an epidemic disease, which means it grows exponentially. One case today will be five cases in a few days and then 25 cases and so on. So, time is of the essence, and the sooner you can identify epidemics, the better the prospect of stamping it out and preventing global spread.

Imagine if the COVID-19 pandemic had been detected well before it spread around the world, when there was only a handful of cases contained within a small geographic location?

Isolating cases and tracing and quarantining their contacts may have been enough to stop it spreading.

Exponential growth and time are the enemies we face with epidemic diseases the longer we take to act, the larger the epidemic will become and over a very short period. Just look at the Sydney outbreak which started in Bondi in June 2021.

Recall the West African Ebola epidemic in 2014. It was 67 times the size of the largest previously recorded Ebola outbreak, it reached urban areas, and killed more than 11,300 people.

Ebola outbreaks can kill 25 to 90 percent of those infected. In 2014-15, hundreds of health workers died, decimating the already-struggling healthcare systems of Liberia, Guinea, and Sierra Leone. Medecins Sans Frontieres (MSF) responded in each of these contexts.

In the Ebola outbreak, with fears of a pandemic on the horizon, organisations like the World Health Organization (WHO), MSF, and others supported national health systems by treating and isolating patients; tracing and follow up of patient contacts; raising community awareness of the disease such as how to prevent it and where to seek care; conducting safe burials; proactively detecting new cases; and supporting existing health structures.

When WHO was first notified of Ebola in March 2014, it may have comprised a few 100 cases, but it grew exponentially. By August 2014, the case numbers were in the thousands, and by October over 20,000 cases had occurred.

Furthermore, until only very recently, there were no tools to prevent or treat Ebola. Today a preventive vaccine and curative drugs are available. Imagine how many lives could have been saved if the epidemic had been detected when there were only a handful of cases.

Prior to COVID-19 in 2019, the Ebola epidemic saw the fastest trajectory to development of a vaccine, with Phase 1 trials in Oct 2014 to the approval of this vaccine in Nov 2019. Indeed, the average time was 10-15 years prior to both COVID-19 and Ebola vaccine developments.

For COVID-19, vaccines were developed and ready for use in less than 12 months, but after devastating global consequences of the pandemic and the hundreds of thousands killed in the global north.

In short, it is unwise to rely solely upon vaccines and or/ their development to manage an epidemic, especially in low-resource settings. Non-pharmaceutical interventions such as testing, tracing and measures to reduce contact between people are also important.

We have had a measles vaccine since the 1960s, however the disease rages through the world in epidemic proportions in over 41 countries such the Democratic Republic of Congo and Central African Republic.

The primary reason behind this is a deeply inequitable, and unfair global biomedical system which has unfairly provided for wealthier countries but not low-income countries.

We are seeing it play out with COVID-19, where only 1.8 per cent of people in low-income countries have received one dose, out of 5.4 billion doses administered globally.

With COVID-19, the general public have had a taste of what epidemiologists have known for decades, that strong health surveillance is essential to getting on top of outbreaks and to have any chance of zero elimination strategies, or any suppression strategy for that matter, working.

While the global disparity of vaccination rates persists, what new technologies is Australia investing in to helping communities get on-top of outbreaks and bolster health surveillance?

How are we harnessing artificial intelligence together with the proliferation of the internet and social media for early detection of epidemics?

The usual way we identify epidemics is through public health surveillance which is when labs or doctors notify health authorities of unusual, serious, or notifiable infections.

When lots of these notifications start piling up, or a trend is seen of higher case numbers than usual, the health official may investigate a possible outbreak.

But people talk about illness in their communities, and local news agencies report on unusual outbreaks, long before health officials know about it.

What if we could mine the vast, un-curated public data available to us in this digital age and detect signals of epidemics early?

At UNSW, the EpiWatch observatory does just that, tapping into news reports from around the world, in many different languages, using algorithms and artificial intelligence (AI) to detect early outbreak signals.

We showed that a signal for unusual pneumonia in China could have been detected in November 2019; and CSIRO research showed that the Ebola epidemic in West Africa could have been detected three months before WHO was aware of it.

This is in no way a replacement for in-country based data collection or existing Early Warning, and Alert Response Systems (EWARS). Using AI in epidemiology is an additional tool that uses innovative technologies and has the potential to reach communities who do not have the strongest national health surveillance systems.

It can also overcome censorship of information to detect signals in countries that are withholding outbreak information from the world. Reasons for censorship include fear of impacts on tourism, trade, or other parts of the economy, or political reasons.

Traditionally, declaring epidemics rest solely on the responsibility of governments, but never in human history has there been more attention on virology and epidemiology from the public. Therefore, ensuring that data-collected from the internet follows scientific modelling and surveying has never been more important.

As with most emergent technology using data and information to inform a product, the ethics over use of open-source data and safe-guards will need to be in place on who this empowers. Generally, however, methods such as used by Epiwatch do not utilise identifying or private information.

Moving forward the Kirby Institute at UNSW, with CSIRO Data 61 is exploring with MSF on how best AI can be utilised to detect epidemics as fast as possible and give vulnerable communities in low-income countries a fighting chance when epidemics strike.

Applying our lessons learnt from the COVID-19 pandemic and Ebola, now is the right time for Australia and the humanitarian community to invest in innovative health surveillance systems, and to keep potential epidemics isolated to save lives.

Professor Raina MacIntyre is NHMRC Principal Research Fellow and Professor of Global Biosecurity. She heads the Biosecurity Program at the Kirby Institute, which conducts research in epidemiology, vaccinology, bioterrorism prevention, mathematical modelling, genetic epidemiology, public health and clinical trials in infectious diseases.

Arunn Jegan is Advocacy Coordinator at Mdecins Sans Frontires (MSF) Australia. He is also the Permanent Facilitator for the emergency public health course at Epicentre in Paris. Arunn has worked as Head of Mission and Emergency Coordinator and has worked in Yemen, Syria, Venezuela, Bangladesh for MSF and in Afghanistan, Iraq, Jordan, Lebanon, and Turkey in senior management positions for other international NGOs. He specialises in social research, conflict/political analysis, complex project management, and humanitarian crisis coordination of public health emergencies.

See the Croakey Conference News Service coverage from the World Congress of Epidemiology.

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Harnessing artificial intelligence to help prevent epidemics before they spread - Croakey Health Media

Cybercriminals are using artificial intelligence (AI) to evolve the sophistication of attacks at a rapid pace. In response, an increasing number of organisations are also adopting the technology as part of their cybersecurity strategies. According to research conducted in Mimecasts State of Email Security Report 2021, 39 per cent of organisations are utilising AI to bolster their email defences.

Although were still in the early phases of these technologies and their application to cybersecurity, this is a rising trend. Businesses using advanced technologies such as AI and layered email defences, while also regularly training their employees in attack-resistant behaviours, will be in the best possible position to sidestep future attacks and recover quickly.

Mimecast is integrating AI capabilities to help halt some of cybersecuritys most pervasive threats. Take the use of tracking pixels in emails, for example, which both BBC and ZDNet have called endemic. Spy trackers embedded in emails have become ubiquitous often by marketers but also, increasingly, by cybercriminals looking to gather information to weaponise highly targeted business email compromise attacks.

Mimecasts CyberGraph uses machine learning, a subset of AI, to block these hard-to-detect email threats, thus limiting reconnaissance and mitigating human error. CyberGraph disarms embedded trackers and uses machine learning and identity graph technologies to detect anomalous malicious behaviour. Because the AI is continually learning, it requires no configuration, thus lessening the burden on IT teams and reducing the likelihood of unsafe misconfiguration. Plus, as an add-on to Mimecast Email Security, CyberGraph offers differentiated capability integrated into an existing secure email gateway, streamlining your email security strategy.

AI is here, and here to stay. Although its use is not a silver bullet, theres a strong case for it in the future of cybersecurity. Mimecast CyberGraph combines with many other layers of protection. It embeds colour-coded warning banners in emails to highlight detected risks, and it solicits user feedback. This feedback strengthens the machine learning model and can update banners across all similar emails to highlight the new risk levels.

As more cyber resilience strategies begin to adopt AI, it will be vital that people and technology continue to inform one another to provide agile protection against ever-evolving threat landscapes. Innovations such as CyberGraph provide evidence that AI has a promising value proposition in cybersecurity.

Duncan Mills is the senior product marketing manager at Mimecast

This article was originally published in the Summer2021 issue of The Record. To get future issues delivered directly to your inbox, sign up for a free subscription.

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Artificial intelligence is the future of cybersecurity - Technology Record