AI in Medicine

Doctors, big data and AI

By Professor David Playford, Cardiologist, Perth

Will doctors be replaced by artificial intelligence (AI)? The answer is a resounding, NO! However, doctors not using AI may be replaced by those who do.

AI means a machine with humanlike intelligence. Machine learning is one form of AI, which (after training) enables the software to make simple predictions based on associations it identifies within complex data. It does not rely on simple equations. A person instantly recognises a cat, dog, or horse. For a computer to recognise different animals, it requires computer training or recognition system. It could create a formula based on identifying two ears, two eyes, a nose, a mouth and some whiskers, which will reliably identify all three as being the same animal.

In contrast, machine learning is analogous to training a small child. The software is shown many images of a cat (labelled ‘cat’), ‘dog’ and ‘horse’. The software performs its

Key messages

AI and big data are here Well-used AI can enhance medical practice Doctors will need to embrace and manage AI.

Multiparameter Space – how AI thinks

own feature recognition (using an ‘artificial neural network’) identifying aspects unique to each known label. More advanced feature recognition systems can classify unlabelled data first, simplifying the AI training process.

Machine learning does not involve the system being directed which features to identify. The software identifies these for itself. Exactly how and which features are used, is not revealed (‘black box’). This is similar to human learning.

We need AI Strategically applied, AI is ideally suited to organising the large amounts of seemingly disparate data generated from medical testing. We cannot do this ourselves. Relative to a computer, humans have poor memory, poor mathematical skills, innate bias, poor recall accuracy and poor capacity to analyse big data in a multidimensional space. Humans can see in three dimensions and only imagine a multidimensional space.

AI can work in as many dimensions as it is given. Humans are superior to computers in other areas (e.g. capacity for abstract thought, consciousness, empathy). It makes sense, therefore, that the best application of AI to medicine should to extend our capacity, not compete with it.

AI has been applied around image recognition and analysis of big data. Our research in cardiology has focused on analysis of big data and automation of the process of diagnosis, to improve efficiency, accuracy, and consistency. Our AI system for automated diagnosis of arrhythmias (e.g. AF) is just reaching human-level accuracy but with greater consistency and speed.

Our automated system for interpretation of echocardiographic data has impressive accuracy in automatic diagnosis of aortic stenosis, including the phenotype of abnormalities associated with AS and future mortality.

In this emerging area we need to be ready and willing to embrace AI, train it to perform complementary tasks, and then in turn, learn from what the AI can teach us. Then we can truly be synergistic with this exciting technology.

Author competing interests – Prof. Playford is Medical Director of Alerte Digital Health, a medical AI company. He is principal investigator of the national Echo Database Australia.

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WA leads the pack

Since 2013, the program’s resources across 23 topics have been accessed more than 22,000 times. In addition, many have attended in-house workshops, and resources have contributed to university teaching programs. The Royal Australasian College of Physicians (RACP) links to the program’s content as an additional resource for their Online Learning Research Projects course. The program also actively cross-promotes next-level research skills training opportunities.

The following are currently available via the CAHS website at pch.health.wa.gov.au/ ResearchEducationProgram: • The schedule for the 2020 Research Skills Seminar Series • Recordings of all 2019 seminars, plus handouts • The Clinical Audit Handbook – also useful for other types of surveys • Subscription to the mailing list for upcoming events and registration for upcoming seminars Site coordinator access – individuals with videoconferencing facilities can organise group access to seminars on the day, with materials sent in advance, including evaluation forms and handouts. • Workshop-related online resources eg: Research Electronic Data Capture use.

The CAHS Research Education Program continues to add new topics and resources and welcomes content suggestions via ResearchEducationProgram@health.wa.gov.au.

ED: A/Professor Skull is a paediatrician, clinical epidemiologist, public health physician and head of the CAHS Research Education Program.

ADVANCEMENT OF COCHLEAR IMPLANT TECHNOLOGY

Andre Wedekind M.Clin.Aud., BHSc (Physiotherapy) Anne Gardner Post Dip. Aud., BSc Cochlear implants (CI) have been at the edge of bionics innovation for 30 years, providing hearing to those with very severe hearing losses through direct stimulation of the auditory nerve.

There are two components of a cochlear implant. The implant Internal, implantable structures of a CI system are becoming smaller and, together with surgical advances, build on a trend towards atraumatic surgery and increasing structure preservation. Cochlear implantation no longer means losing all residual natural hearing. For many, it simply replaces the speech frequencies for which hearing aids are no longer sufficient. This has led to an unprecedented growth in candidacy. Recipients with a combination of natural and CI hearing show best performance in understanding speech in noise, music appreciation and sound localisation. With more candidates, the known benefits of implantation from a young age, and a growing likelihood for an MRI over a lifetime, MRI compatibility is of increasing importance. Some current models are now compatible with a routine 3 Tesla MRI without the need for surgical magnet removal. The sound processor Sound processing in the externally worn component has improved significantly. Manufacturers have drawn from the technological advancements from within the hearing aid industry and utilised innovations for better speech-in-noise listening, automatic responding to changing environments, and connectivity to wireless devices. In addition, some CI processors can connect directly to smartphones allowing listeners to stream to a compatible hearing aid in the opposite ear at the same time, maximising the benefits of binaural hearing for a full range of multimedia. Compared to people with normal hearing, cochlear implant recipients still require significantly higher signal-to-noise ratios to achieve the same understanding. To improve comfort and performance in noise, the processor automatically detects, analyses and reduces non-speech sounds such as wind, traffic or clanging dishes. They combine multiple microphones and frequency analysis to zoom towards the dominant speech signal and away from potentially distracting speakers in a crowd.