Artificial intelligence Part 6: specific industry impacts - healthcare

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The impact of AI on healthcare is more nuanced and varied than most other white collar sectors. Healthcare is more complex due to need to retain a strong physical human presence in the medical and care functions that cannot be automated or replaced by digital technology. Healthcare has a complex set of regulations, legal liability and an irreducable human dimension with doctors, nurses, allied health professionals required for direct patient face-to-face contact inclusive of the use of tele-health services.

In contrast, administrative and diagnostic supportive functions are highly exposed. A summary is provided below and is not exhaustive -

Clinical diagnosis and decision support

• Radiology already has AI systems that match or exceed radiologists in detecting certain cancers (breast, lung, skin). The aspect of concern is potential over diagnosis due to the sensitivity of the digital systems used. The radiologist role is shifting towards oversight, complex cases handling and AI exception management. The potential risk in this field being the volume of radiologists needed to undertake radiology functions may reduce.
• Pathology has a similar pattern to radiology as AI can analyse tissue samples at scale. The role of pathologists however is not removed at this time but is being augmented.
• Diagnostic support to General Practitioners can be provided through AI tools that synthesise patient medical history, symptoms and test results. These AI tools are being deployed already however the intention is to support the medical service provided by doctors to patients not substitute it. A secondary intention is to reduce the need for specialist referrals however this has yet to be achieved.
• Dermatology and ophthalmology are two specialties that are heavily dependent on pattern recognition and will face some AI encroachment, however as with other diagnositic tools it may be a supportive function not a medical role replacement one. 

Clinical administrative functions and documentation

• Medical transcription is already largely automated with voice-to-text using clinical AI being widely used.
• Clinical note writing is being addressed by ambient AI scribes such as Nuance DAX. Documentation can consume 30-40% of physician time and assists medical practitioners to achieve quality of life improvement however it reduces medical transcription services significantly, if not in some cases, entirely.
• Prior authorisation, coding and billing  are very large cost centres and are being progressively automated and threatening large administrative workforces in hospitals and insurance companies.

Nursing and Allied Health

• Triage and patient monitoring: AI can monitor patient vital signs, identity and alert to deterioriation and prioritise nursing care. AI provides service augmentation but not replacement of front line nursing care which must be physically provided.
• Care coordination roles do face pressure from AI that can track patient journeys, identify gaps and schedule follow-ups. At this time however this remains an augmentation tool rather than a job replacement one. 
• Bedside care, emotional support and physical nursing are strongly human services and cannot be replaced by AI. It remains one of the most protected areas across all industries. 

Pharmaceuticals and medical science/research

• Drug discovery timelines are being compressed by AI which reduces some research roles but creates new roles in AI-guided drug design. An example of AI impact is Alphaford's protein structural predictions which transformed structural biology.
• Clinical trial design and patient matching is being assisted by AI but not replaced by it. 

In healthcare, it is the administrative organisational pyramid that is being compressed with headcount reduction. Clinical roles continue with signifcantly increased volumes of patients possibly over time.

ANZAC Day 2026

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ANZAC Day continues to have strong public support in recognition of the service of men and women during time of war. This special commemorative day has been held for 110 years on the 25th April and was originally intented to honour the members of the Australian and New Zealand Army Corps who served in the Gallipoli campaign in 1915 (World War I). Since that time, it has expanded to include other conflicts and peace keeping operations until the present time. On this day, those who lost their lives as a result of their service are particularly remembered.

Lest we forget

Science: the mind and imagination

 

How does imagination really work in the brain? New theory upends what we knew

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Thomas Pace, University of the Sunshine Coast and Roger Koenig-Robert, University of Technology Sydney; UNSW Sydney

Your brain is currently expending about a fifth of your body’s energy, and almost none of that is being used for what you’re doing right now. Reading these words, feeling the weight of your body in a chair – all of this together barely changes the rate at which your brain consumes energy, perhaps by as little as 1%.

The other 99% is used on the activity the brain generates on its own: neurons (nerve cells) firing and signalling to each other regardless of whether you’re thinking hard, watching television, dreaming, or simply closing your eyes.

Even in the brain areas dedicated to vision, the visuals coming in through your eyes shape the activity of your neurons less than this internal ongoing action.

In a paper just published in Psychological Review, we argue that our imagination sculpts the images we see in our mind’s eye by carving into this background brain activity. In fact, imagination may have more to do with the brain activity it silences than with the activity it creates.

Imagining as seeing in reverse

Consider how “seeing” is understood to work. Light enters the eyes and sparks neural signals. These travel through a sequence of brain regions dedicated to vision, each building on the work of the last.

The earliest regions pick out simple features such as edges and lines. The next combine those into shapes. The ones after that recognise objects, and those at the top of the sequence assemble whole faces and scenes.

Neuroscientists call this “feedforward activity” – the gradual transformation of raw light into something you can name, whether it’s a dog, a friend, or both.

In brain science, the standard view is that visual imagination is this original seeing process run in reverse, from within your mind rather than from light entering your eyes.

So, when you hold the face of a friend in mind, you start with an abstract idea of them – a memory or a name, pulled from the filing cabinet of regions that sit beyond the visual system itself.

That idea travels back down through the visual sequence into the early visual areas, which serve as your brain’s workshop where a face would normally be reconstructed from its parts – the curve of a jawline, the specific shade of an eye. These downward signals are called “feedback activity”.

A signal through the static

However, prior research shows this feedback activity doesn’t drive visual neurons to fire in the same way as when you actually see something.

At least in the brain regions early in the vision process, feedback instead modulates brain activity. This means it increases or decreases the activity of the brain cells, reshaping what those neurons are already doing.

Even behind closed eyes, early visual brain areas keep producing shifting patterns of neural activity resembling those the brain uses to process real vision.

Imagination doesn’t need to build a face from scratch. The raw material is already there. In the internal rumblings of your visual areas, fragments of every face you know are drifting through at low volume. Your friend’s face, even now, is passing through in pieces, scattered and unrecognised. What imagining does is hold still the currents that would otherwise carry those pieces away.

All that’s needed is a small, targeted suppression of neurons that are pulled by brain activity in a different direction, and your friend’s face settles out of the noise, like a signal carving its way through static.

Steering the brain

In mice, artificially switching on as few as 14 neurons in a sensory brain region is enough for the animal to notice it and lick a sugar-water spout in response. This shows how small an intervention in the brain can be while still steering behaviour.

While we don’t know how many neurons are needed to steer internal activity into a conscious experience of imagination in humans, growing evidence shows the importance of dampening neural activity.

In our earlier experiments, when people imagined something, the fingerprint it left on their behaviour matched suppression of neuronal activity – not firing. Other researchers have since found the same pattern.

Other lines of evidence strengthen our theory, too. About one in 100 people have aphantasia, which means they can’t form mental images at all. One in 30 form these images so vividly they approach the intensity of images we actually see, known as hyperphantasia.

Research has found that people with weaker mental imagery have more excitable early visual areas, where neurons fire more readily on their own. This is consistent with a visual system whose spontaneous patterns are harder to hold in shape.

Taking all this together, the spontaneous activity reshaping hypothesis – our new theory that imagination carves images out of the steady stream of ongoing brain activity – explains why imagination usually feels weaker than sight. It also explains why we rarely lose track of which is which.

Visual perception arrives with a strength and regularity the brain’s own internal patterns don’t match. Imagination works with those patterns rather than against them, reshaping what is already there into something we can almost see.

Thomas Pace, Researcher and Lecturer at the Thompson Institute, University of the Sunshine Coast and Roger Koenig-Robert, Senior Research Fellow, Graduate School of Health, University of Technology Sydney; UNSW Sydney

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Earth Day 2026

 

Artificial intelligence Part 5: specific industry impacts - finance and banking

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AI is particularly suited to back office operations in banks and financial institutions analysing large amounts of financial data. Typically banks and financial institutions employ large numbers of people to undertake functions such as compliance documentation, fraud review, transaction monitoring, credit analysis and financial reporting. AI systems designed by Palantir Technologies and SAS Institute for example, can review financial data and identify anomalies much faster than manual teams. The impact of AI on specific industry segments is summarised as follows -

Investment banking and capital markets

• Analyst roles are vulnerable to AI systems. The business folklore of junior bankers working 100 hour weeks using Excel models, pitch books and risk management due diligence is already under severe pressure as these tasks are highly structured and can be executed by AI. Examples already known include Goldman Sachs and JPMorgan deploying AI for financial modelling, earnings analysis and report generation.
• Equities market research has been transformed as AI can monitor thousands of stocks, synthesise earnings and generate initial research notes faster than any human team. 

Asset management

• Quantative analysis and factor modelling can be easily augmented by AI and is increasingly occuring already. This situation is leading to a changing and evolving role for quantitative analysts.
• Portfolio reporting and client communication is increasingly being automated with AI at the commodity end.
• Active investment funds management comes under further pressure as passive funds are now better guided by AI-driven strategies.
• Compliance reporting which is a very large cost centre in financial markets is being substantially automated with AI. The use of automation was an existing trend for many years but AI enables a faster rate of uptake. 

Retail and commercial banking

• Loans underwriting is already largely algorithmic and automated for retail consumers and the SME business level already. AI does not alter the trend but merely further reduces the remaining human review layer.
• Customer service and branch banking continues a long decline with face-to-face service reduction. This situation however is subject to fluctuations due to community pressure and increasing consumer preferences for personal interaction for specific services. AI's influence is limited in this line of business activity.
• Fraud detection and ani-money laundering (AML) monitoring is already within the AI-dominated sphere. Human reviewers have been shifting to exception handling only.
• Financial advice at the mass market level  already has limited use of robo-advisers. This segment is however subject to regulation and government oversight and the requirement for financial advice licenses, accountability and legal liability. The use of robo-advisers beyond limited information provision and recommendations for the mass retail market has not yet occured. High-net individuals particularly prefer human advisers and personal banking managers rather than an automated service. Various financial advice scandals in the sector may also limit the use of AI for the time being.

As with all industries, the use of AI in finance and banking is most easily implemented in large data analysis, administrative and reporting tasks. It is not well suited to client relationships and regulatory, legal and compliance responsibilities.

Artificial intelligence Part 4: specific industry impacts - graphic arts and visual design

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Graphic arts and visual design are another industry that is heavily exposed to AI particularly with impacts such as hierarchical pyramid compression. Tasks and projects that once needed a team of junior artists can now be completed by a single art director using AI tools. AI image systems can now produce concept art, advertising visuals, books covers, storyboards and marketing graphics. Specific industry segments affected are discussed as follows -

Commercial illustration and stock art

• Stock photography and illustration is already heavily impacted. Companies such as Shutterstock, Getty Images and Adobe all now offer AI image generation. The market for generic commercial illustration has largely collapsed for independent artists.
• Illustrators who designed books covers, editorial art and advertising assets, mainly mid-tier commercial work, now face severe income compression. This blog uses AI generated images having once held accounts with commercial image suppliers such as Shutterstock.

Advertising and brand design

• Mood boards, concept art and campaign mockups are increasingly AI-generated at the brief stage.
• The jobs of junior designers whose purpose is to execute pixel-perfect images under senior creative direction are now heavily at risk as these tasks are automatable.

UI/UX design

•  AI tools: Such as Figma AI can automate layout generation, component creation and user flow suggestions. Junior UI designers who develop wirseframes face significant automation pressure.
• UX research such as interviews, synthesis and insight generation remain more protected however even parts of these processes such as synthesis and pattern recognition can be managed through AI.

The multi-part series covering AI, published in this blog, has been researched and compiled using Claude ai (Anthropic), ChatGPT (OpenAI), and Grok (Xai).