Artificial Intelligence in Healthcare: Friend or Foe?

Updated: Jun 27

Artificial intelligence was at one time an abstract idea that lived in the imaginations of science fiction enthusiasts. It is, however , the nature of mankind to push boundaries and make what is conceived in imagination into a tangible reality. Although the idea of artificial intelligence can be and has been applied to a number of industries world wide , its applications in healthcare are the most widely debated .

Artificial intelligence is based on the idea of deep learning , which uses architectures such as deep neural networks, deep belief networks and recurrent neural networks in applications like computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics and drug design, where they have produced results comparable to and in some cases superior to human experts.

In theory AI systems need to “see” unstructured data, such as radiologic images, graphs, slide images, clinic notes, and literature texts, and then convert this to a knowledge base in a structured format. Companies worldwide are taking up the challenge of applying the principle to a complex and some would say , flawed healthcare system, in an attempt to streamline processes and establish order in a seemingly subjective, often chaotic science.

The focus of the tech industry has unsurprisingly been on clinical diagnostics and its applications in radiology and pathology . The idea is simple- to handover repetitive and time consuming tasks to a machine , so that the human experts can focus on the more challenging and complex cases.

Labour intensive and manual skill based tasks have been reduced with the advent of fully automated analysers and machines. Digitalisation of pathology , with the introduction of telepathology and whole slide imaging led to faster diagnosis, giving rural areas access to these services and improving turnaround time. The introduction of e-health records has greatly helped with providing healthcare providers with patients comprehensive history and progression of treatment, and lets fewer patients fall through the cracks. It has also led to the reduction of patient identification errors .

Radiologists and pathologists diagnose by training the eye to recognise patterns and correlating them with clinical symptoms to come to a diagnosis . With the evolution of technology however, the amount of data that is generated, either from automated analysers in pathology or the numerous images in a Pan Scan , is so voluminous, that diagnosticians, once maestros with a chest radiograph or a peripheral smear are now often visually fatigued Deep learning , the core of AI , in the area of diagnostics is an autodidact- like an outstanding pathology or radiology resident, the more images or slides it analyses, the better it gets.

The next step naturally progressed to the analytical aspects of diagnostics - how do we introduce technology in this sector to make the analysis and interpretation of clinical and diagnostic data simpler , faster and more effective ? Tech companies worldwide actively took up the challenge.

It is clear that artificial intelligence is geared to take the healthcare industry by storm. However, there are some who remain cautious about this disruption. One cannot help thinking - if the machine can do all the mental heavy lifting , what are the doctors going to do ? Healthcare has always been an industry based on human contact and interaction. Human biology is a variable and inexact science and it requires physicians to collaborate and put together clinical observations and diagnostic data together to arrive at a conclusion. Clinical observations are honed by experience and are based on the physicians interaction with the patients , as well as visual and auditory cues that are picked up , sometimes subliminally . Can an algorithm really replace this?

There is also the issue of employment to consider. The healthcare sector is responsible for the employment of physicians, physician assistants, medical transcriptionists and technicians. Even though it leads to lower manpower costs for the industry and is an efficient business practice , it will lead to the future generations rethinking of career and education options and will change the job market as we know it. It also raises the question of accountability . At what point can we say : " It wasn't me ! It was the machine !" ?

In India, even in Metropolitan cities , the idea AI seems far fetched and eons away . However, there are industry magnates who have already jumped onto the tech bandwagon. The Apollo group has been a strong advocate for the use of technology in healthcare and remains at the forefront of integration .

It is an exciting, historically disruptive time in healthcare .The idea that it is “ physician versus artificial intelligence” however , is counter productive. The best scenario is a synergistic relationship between the two, which showcases the best of both applied sciences, in order to do what the healthcare industry is called upon to do - improve the lives and the health of its patients.

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