BBC In Depth: the ongoing battle to define autism

What if... there turned out to be more autistics than NT's? Yippee!!!!!!!

• Checking the Machine's Manual: Like many people in the UK, my own diagnosis was assessed using the American DSM-5 framework, but the NHS also relies on the WHO (ICD-11) framework for its wider health records. A major problem with an AI trained purely on the DSM is that it treats diagnosis like a rigid, binary checklist—if someone scores one point less than the cut-off, a machine simply says 'no'. A machine cannot understand what the WHO framework highlights: that a person might pass a rigid checklist but still experience severe hidden struggles because their environment isn't accommodating.
• Spotting Overlapping Conditions: An AI looking at pure data points can easily mistake dyslexic processing delays or ADHD traits for an autistic communication difference. A human clinician brings the holistic nuance needed to unpick how these different traits interact in real life.
• Freeing up Humans for 'People Work': Right now, a massive chunk of an assessment is just administrative paperwork, scoring questionnaires, and transcribing histories. If AI acts as the calculator to handle that data entry, it frees up clinicians from behind their desks. In my view, it lets human experts spend their limited time actually being people with the people.
• It Pays for Itself: Using AI as a screening assistant to sort initial data could drastically cut the time and cost per assessment. I would love to see those saved resources funneled directly into cutting down brutal waiting lists and funding actual, real-world support services.

I'm not so sure about AI being trained to test for Autism, it depends on what data it's fed to enable it to make a diagnosis and I think that could become very biased very easily.

Will it say no if you score one point less than for a "positive" mark?

How will it take into account other things like dyslexia?

A human could say someones borderline or has some strong suggestive features of autism or whatever gobbledygook they use and make provision accordingly, but will AI have that level of discernment?

Will different AI's have differnt criteria, it's confusing enough now with things like the DSM being differently interpreted in the US than in Britain?

I think we need more actual people, humans doing this stuff, not turning it over to machines and when the AI spits out its results will people still be waiting for anything to actually be done? Will the AI deliver help accessing services, where they exist and sorting out the post code lottery?

I'm not so sure about AI being trained to test for Autism, it depends on what data it's fed to enable it to make a diagnosis and I think that could become very biased very easily.

Will it say no if you score one point less than for a "positive" mark?

How will it take into account other things like dyslexia?

A human could say someones borderline or has some strong suggestive features of autism or whatever gobbledygook they use and make provision accordingly, but will AI have that level of discernment?

Will different AI's have differnt criteria, it's confusing enough now with things like the DSM being differently interpreted in the US than in Britain?

I think we need more actual people, humans doing this stuff, not turning it over to machines and when the AI spits out its results will people still be waiting for anything to actually be done? Will the AI deliver help accessing services, where they exist and sorting out the post code lottery?

• Checking the Machine's Manual: Like many people in the UK, my own diagnosis was assessed using the American DSM-5 framework, but the NHS also relies on the WHO (ICD-11) framework for its wider health records. A major problem with an AI trained purely on the DSM is that it treats diagnosis like a rigid, binary checklist—if someone scores one point less than the cut-off, a machine simply says 'no'. A machine cannot understand what the WHO framework highlights: that a person might pass a rigid checklist but still experience severe hidden struggles because their environment isn't accommodating.
• Spotting Overlapping Conditions: An AI looking at pure data points can easily mistake dyslexic processing delays or ADHD traits for an autistic communication difference. A human clinician brings the holistic nuance needed to unpick how these different traits interact in real life.
• Freeing up Humans for 'People Work': Right now, a massive chunk of an assessment is just administrative paperwork, scoring questionnaires, and transcribing histories. If AI acts as the calculator to handle that data entry, it frees up clinicians from behind their desks. In my view, it lets human experts spend their limited time actually being people with the people.
• It Pays for Itself: Using AI as a screening assistant to sort initial data could drastically cut the time and cost per assessment. I would love to see those saved resources funneled directly into cutting down brutal waiting lists and funding actual, real-world support services.