Candidates will provide their resumes during the interview.
Over the past few years, the topics of artificial intelligence and machine learning have been extremely popular and have established themselves as the mainstay of discussion in most homes and workplaces. Without a doubt, the industry is growing exponentially in a relatively short amount of time, perpetuated by a large number of players, large and small, who are actively seeking to capture market share.
In addition to this explosion of growth, there is a huge demand for the right people and labor to accustom and develop these products. However, despite demand, the standards for getting jobs with artificial intelligence and machine learning have become more stringent than ever before. This is especially true for healthcare AI jobs.
Conservatively, the AI market is expected to reach nearly $267 billion by 2027, with a CAGR of nearly 37%. In particular, the market capitalization of healthcare AI should increase by more than 10 times over the next eight years. Therefore, this figure also shows that AI and ML-related jobs have increased by nearly 74% per year over the past four years, indicating that the market is hungry for talent to heal innovation needs.
However, these numbers could be misguided to believe that many jobs are available to healthcare AI enthusiasts and that there are many opportunities for newcomers to work in this field. This is no more fake. In fact, despite the heavy demand placed on the current workforce for talent in these fields, employment standards for artificial intelligence and machine learning are becoming increasingly difficult. Candidates for this work are expected to have a confluence of expertise in a variety of fields, from programming and IT architecture to understanding at least the baseline of computing engineering and neural networks. Furthermore, the more advanced roles that lead the product team or actually oversee the development of basic and large-scale language models are increasingly granted to academics and industry stubborns who have previously pursued work with research or more academic capabilities. This is especially true in the field of healthcare, where complex industry knowledge is required to apply to the basic principles of AI.
For example, both alphabet companies, Isomorphic Labs and Google Deepmind, have been working on the intersection of healthcare, technology and biology for many years. This work has recently announced the latest iteration of the AlphaFold tool. It utilizes the best machine learning and advanced basic models to advance in the fields of proteomics, chemistry and biology. It may seem like an overnight success, but this innovation took years, if not decades, and its foundations, and began in a lab setting. Thus, the leadership behind many tasks reflects a congruent and academically pedigree.
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Another example is Apple, which is healthcare work. Apple is currently recognized for Titan, an artificial intelligence in conjunction with its suite of products for consumer healthcare, but for many years the company has relied on the talent of clinicians and healthcare professionals to help develop technology and workflows. Now, that baseline knowledge brings this fundamental task and the means to integrate with usable artificial intelligence, models and products for consumers requires veteran experts to transform ideas into viable products, both from academic and commercial backgrounds.
Therefore, despite the growth and increased demand, companies are highly selective in hiring candidates to work in this field, making this one of the most competitive career areas ever. Without the right constellations of research, experience and hard skills in this field, it is almost impossible to get a job with AI. In fact, “It has been shown that the majority of business leaders (66%) do not consider hiring candidates who do not have AI skills…71% of executives say they prefer to hire candidates with AI skills, even if they have less experience than experienced candidates who lack AI aptitude.”
At the rate at which these sectors are growing, the future holds a lot of promise when it comes to AI and ML work. Both educators, innovators and institutions need to be prepared to meet the demands and needs of the modern workforce.
