In these columns, I have earlier attempted to highlight the possibilities of multiple game-changing applications in the field of artificial intelligence (AI) that hold the potential to deliver positive benefits to humanity. AI can do much more beyond enabling a transformative business impact. Every technological advancement brings with it an opportunity to deliver positive changes to society. It is no different with AI. With massive increases in computing power and data deluge, it is possible for AI to be a harbinger of change for the society we live in.

A complex problem that we see is Autism Spectrum Disorder. As of 2015, this neurodevelopment disorder is estimated to have affected the lives of 24.8 million people globally. Worse, despite all the advances in medical sciences, there is no conclusive understanding of its causes and cures. While we do see cases where autism gets resolved, we are still unable to point concretely to any medical option that works better than others.

At best, autism can be managed – possibly through a mix of early diagnosis and continuous therapy. Thankfully, we live in an era where the awareness of autism is on the rise and the associated stigma is declining. By harnessing the power of AI to detect and manage cases of autism, we could potentially help those suffering from it lead a fulfilling life of dignity and respect.

While not conclusively proven, it is likely that an early diagnosis of autism is hugely beneficial for managing it in the future. An early detection would also help the people around these children be better attuned to their condition and enable them to set an environment that is conducive to their development.

One such application is applying AI to analyse vocal and behavioural cues exhibited by children. Stephen Sheinkopf — an autism researcher and psychologist at the Brown University Center for the Study of Children at Risk — believes anomalous crying patterns of babies might serve as an early warning sign of autism.

Neurological cues present in the acoustic features of cries — pitch, energy and resonance — might hold the key to early detection. Combining vocal, behavioural and physiological data, we might be able to piece together a pathway to early detection. That’s where AI and machine learning can add really great value, in integrating these disparate pieces of information that might otherwise be hard to make sense of.

An excellent example of this in action is Chatterbaby — conceptualised and developed by Ariana Anderson, a computational neuropsychologist at UCLA. Earlier, functionality enabled identification of potential causes for why a baby is crying by monitoring crying patterns. In addition, Chatterbaby is also attempting to identify if there are discernible differences in the crying patterns of autistic children and neurotypical ones — ultimately aiming to isolate the characteristics of each group to detect autism early.

Early ‘Testing’

Researchers in the UK and Italy have turned to AI for developing what might be the world’s first ‘test’ for autism. In their study, they leveraged AI to compare the protein levels in the blood samples of two groups of children — one group comprising 38 children diagnosed with autism, and the other with 31 children without the diagnosis. Their findings helped develop an algorithm that could test autism — with a 90% accuracy for which children have autistic spectrum disorder and an 87% accuracy for which children do not have it.

Another example is through tracking changes in brain function of six-month-old babies, which researchers from UNC Chapel Hill and Washington University’s School of Medicine believe can help early detection. They recently published a paper wherein they examined the brain scans of 59 high-risk babies to understand the connections and interactions between different regions of the brain. Post this, they analysed the brain scans of the 11 babies that were eventually diagnosed with autism.

By combining the data with AI and deep learning, they developed an algorithm able to detect the possibility of autism with an accuracy of 9 out of 11.

Chatbots and Virtual Assistants

Across studies, we see children with ASD have high levels of comfort with computers due to their predictable and logical nature. Autistic children can perceive humans to be emotional and unpredictable but computer-based systems (even those with human expression) to be rational and non-judgmental.

Systems such as chatbots and social robots can help capture and track the progress of autistic children, continuously monitor their social behaviours and make quick, informal assessments in school and at home. The big promise of these systems is allowing autistic children the opportunity to navigate social interactions, unfamiliar environments while aiding them to reach the developmental goals usually set for neurotypical children.

An example of this is an app called Companion, featuring a virtual assistant named Abby. Abby helps identify interests and needs of autistic children and provides support throughout the day. Beyond this app, Identifier identifies talents of autistic children through interactive game-play; eventual results are compiled onto a dashboard detailing the skills and shortcomings of the child.

A Strict No-No

While AI can bring these benefits, it is also important to ensure that these systems are designed with empathy for the autistic population. For instance, the use of bright, jarring colours is an absolute no-no for the design of such applications. Secondly, virtual agents should be enabled to speak in simple language, without the use of idioms, euphemisms or figures of speech to help such people understand instructions much better.

For a problem that has no apparent cause or cure, AI could be a breakthrough in improving the quality of life of those who suffer from autism and the people around them. By using AI for early diagnosis, disease management and people enablement, we would be able to help bring dignity to the lives of affected people.