Why are doctors afraid of AI?
Artificial intelligence (AI) technology has advanced extremely fast in the last years, owing in great part to breakthroughs in machine learning, which is closely tied to data science and statistical prediction. Modeling is involved across several elements of the health sector, including diagnosing, treatment, management, and logistics. Because of the link between machine learning’s capabilities and the needs of the health-care space, it’s widely assumed that AI will have a significant impact.
Books have been written on health AI and many academic and industry conferences have created dedicated AI showcases, demonstrating industry enthusiasm for AI’s potential. In practice, though, the picture is rather different. When looking at which professions in various industries adopts AI skills, healthcare ranks lower.
Some AI algorithms operate in ways that are a bit difficult for the average doctor to comprehend, making it challenging to grasp how they reached a certain outcome. When medical personnel are held accountable for algorithmic judgments, they need to thoroughly understand how the result was obtained. Because very few medical professionals have the necessary training in computer science, most avoid coming into contact with complex software applications.
A different change
At the same time, because the process of training a doctor takes a lot of time, on average over 12 years, followed by another 5 years of more specialization, clinicians in the working field are at the age when it is harder to be enthusiastic about any technological innovation.
Also, while AI in health-care can enhance the quality of treatment, it also raises concerns about data security and social inequality. Despite the fact that the medical field is extensively regulated, there are no laws governing the use of AI in these settings. Several governments across the world, particularly the US and EU, have tried introducing legislation for dictating the use of AI, but none have been implemented fully.
Input and output
The outcomes of artificial intelligence are determined by how it is created and what input is being used. Both data and architecture can be biased, either wittingly or unwittingly. Some critical aspects of a problem may be missing from the algorithm, or the algorithm may be constructed to reflect and reproduce systemic assumptions.
Furthermore, the use of statistics may give the impression that AI is fact-based and accurate, even if sometimes this is not the case. If AI isn’t applied correctly, it could lead to unidimensional results, without a proper analysis.
Another impediment is that doctors need to learn how to use a few new complex applications. This take them out of the ordinary and make them feel easily uncomfortable.
For most hospitals, upgrading outdated equipment with new legacy systems remains a huge barrier. The majority of Artificial Intelligence-based solutions are extremely fast to compute and require a large infrastructure and high-end processors. Because of this, transition costs remain a major factor that is taken into account when integrating new technologies.
The legacy of an old system
Another big concern about AI is that it would result in mass unemployment of human labor when AI workers replace them. A major source of concern is that, while the last wave of automation primarily affected blue collar occupations such as manufacturing, the new generation will primarily affect white collar service-oriented jobs centered on knowledge workers. As the usage of AI expands and pervades the medical world, the demand for qualified human labor in various parts of the health-care field will diminish.
The counterpoint is that many of these technologies aren’t yet capable of reliably replacing a large number of human professions. While AI systems have a lot of capabilities, it can’t operate completely independently. In fact, the majority of effective AI implementations are done in such a way that the AI system serves as augmented intelligence, assisting the human in doing what they do best rather than entirely replacing them.
In general, as technological waves affect industries and workers, job categories are replaced, but overall jobs are not lost. In fact, jobs are expanding and finding new niches, while machines are just replacing old ways of doing things.
Although the technological world has a variety of outstanding solutions, none of them can claim to encapsulate empathy. This is because compassion is all about establishing trust, paying attention to the other person’s emotions and desires, and being sensitive and receptive in a way that makes the other person feel understood.
It will not take our jobs
A patient cannot fully trust a robot or an AI system with a life-threatening decision or even a decision about whether or not to take a medication at this time. This may change in the future as patients take better care of their health and show more responsibility for their own treatment, but we can’t visualize a healthcare system that doesn’t include empathy.
Doctors will always be needed to be in the vicinity of the patients and inform them of life-changing diagnoses. They will rely on specialists to advise and assist them during therapy. That is something that no algorithm or robot could ever replace.
That’s how we came to develop XVision.
Based on AI, our diagnostic algorithms are an indispensable tool for radiologists today.
Our software can be used in many instances. The application can take form as a second opinion when the radiologist needs to double check measurements. It can also be used as a triage mechanism when clinicians need to focus exactly on priority cases in essential periods of time.
It is also an ideal tool for visualizing evolving pathologies at different time moments by quantifying the lesions and comparatively measuring them.
A doctor can use XVision to reduce the time it takes to diagnose each patient and improve the precision of image interpretation, which is especially useful during night shifts or periods of increased workload. We help radiologists by taking care of their administrative tasks, providing a solution that will allow them to focus on what they do best – taking care of their patients.
Our team is dedicated to creating the best digital radiology system for medical imaging analysis.
All of them powered by AI.
We are XVision.
You can also read about how XVision is helping lung cancer detection by clicking here.