Making sense of 'care' in healthcare

Written by: MEHUL H. SADADIWALA, Class of 2023

The highlight of this blog has been the increasing use of Artificial Intelligence (AI) in healthcare and addressing the growing insecurity of medical professionals.

With changing times there has been a growing sense of insecurity amongst medical professionals with their jobs being replaced by Artificial Intelligence (AI), and the rate at which the use of AI has been increasing is to be held accountable. This extensive application of AI in certain fields of medicine (like Radiology, Dermatology, and Pathology) owes to the element of Human error or ignorance that can be conveniently mitigated by well-trained computer models. These unprecedented advancements in AI technology and its growing dependency have  led to a scare among professionals of losing their hard- earned jobs to machines.

Introducing AI systems into healthcare may ease the cognitive burden of drawing diagnostics, and automation of tedious tasks, such as paperwork and computerizing data, could free up time for health professionals which can be diverted towards engaging more directly with their patients.

But this raises the issue that AI systems could be used from an economic point of view to employ lesser staff with lower qualifications since less expertise will  be needed. This can be problematic because if there were to be a technical collapse, the incompetent staff would not be able to recognize any errors, and even if detected, they wouldn’t be skilled enough to compensate for the issue at hand. Thus, it is quite important to have well-trained and practicing experts at your disposal as it became quite evident during the latest pandemic.

The revolutionary input that AI has brought to Heath monitoring and consultations is remarkable. From wearable fitness bands that are quite accurate in detecting arithmetic episodes to the rise of teleconsultations during the pandemic, the new avenue has limitless potential and possibilities.

Figure 1 Artificial Neural Network code to train machine learning model

Photo Credit: Harishil R. Nandwani, NEU, Boston, USA

Radiology, Dermatology, and Pathology are image-abundant specialties and are strongly tied to the use of Deep-learning image processing. A paper was published in 2018, in the journal Annals of Oncology, the results  of which demonstrated that skin cancer could be detected more accurately by an AI system (using deep learning convolutional neural network) than by actual dermatologists. The human dermatologists accurately detected approximately 86% of skin cancers from the images, compared to 95% for the convolutional neural network machine.[1] Similarly, several artificial neural network models have shown accuracy similar to that of human pathologists and radiologists. [2,3]

Simultaneously a bothersome prospect can be of crippling dependency of the new generation of physicians on the judgment of AI which can provide an expedient route of not undergoing the traditional learning which might prove to be of great use in areas deprived of equipment and technology or in situations of technical malfunction. Still, we have not touched on the intense topic of Robotic surgery that carries with it the ability of impeccable micro precision, surpassing the skill of any surgeon that trained for decades.

Although no jobs have been replaced by AI so far, studies have shown that in  the next 10 to 20 years, up to 35% of jobs in the UK are highly susceptible to being replaced by AI. The most vulnerable specialties are the ones dealing with digital information, radiology, and pathology, as opposed to those dealing with the doctor-patient interaction. [4]

The application of AI systems in healthcare may cause healthcare practitioners to feel threatened by this new form of technology, especially if their expertise, autonomy, or authority is challenged. Physicians, Assistant-Physicians & Nurses are HealthCare professionals, and if AI technologies are used to replace healthcare professionals, then there would be a loss of human contact – an essential element of physician-patient relationships. While the usage of AI is only supplemental for the moment, undoubtedly it will become an integral part of future diagnostic practices. What AI brings to the table cannot be overlooked.

The existence of both entities will be interdependent and symbiotic in nature, as without the assurance of qualified physicians the patient would not be able to understand or trust how the AI system arrived at the diagnosis or the treatment plan. Technology is here and it is here to stay, but the element of Care remains irreplaceable and can only be delivered by a fellow human.

“Empathy cannot be uploaded to a device”, this statements holds to be very true as no software on this planet can replace the connection of human touch. The simplest gesture of holding a distressed and anxious patient’s hand for reassurance, offering a warm blanket, or tending to the special needs of a patient can elevate the patient experience in immeasurable ways. Of course, the future is not  Humans vs Machines, but Humans & Machines. But with the rate at which AI has been progressing, it wouldn’t be long that the possibility that certain fields in medicine might soon not hold a lot of value. This may lead to careful consideration of career options keeping the uncertainties of the future in mind.