A candidate provides a resume during an interview.
Over the past few years, the topics of artificial intelligence and machine learning have become extremely popular and have become topics of discussion in most homes and workplaces. There is no doubt that the industry has experienced exponential growth in a relatively short period of time, perpetuated by numerous players, large and small, actively trying to capture market share.
Alongside this explosion in growth has come a gargantuan demand for talent and the right workforce to design and develop these products. However, despite the demand, the standards for getting a job in the field of artificial intelligence and machine learning have never been higher. This is especially true for AI jobs in healthcare.
Research indicates that, conservatively, the AI market is expected to reach a value of almost $267 billion by 2027, with a CAGR of almost 37%. In particular, the market capitalization of AI in healthcare is expected to increase more than 10 times over the next 8 years. Consequently, the Numbers also show that AI and ML jobs have grown nearly 74% annually over the past 4 years, indicating that the market is hungry for talent to meet innovation needs.
However, these numbers may mistakenly lead a healthcare AI enthusiast to believe that there are many jobs available and many opportunities exist for newcomers to this field. This couldn’t be more wrong. In fact, despite the high demand for talent in these fields straining today’s workforce, hiring standards in the areas of artificial intelligence and machine learning have become increasingly difficult. Candidates for this job should have a confluence of expertise in many different disciplines ranging from programming and computer architecture to at least a basic understanding of computer engineering and neural networks. Additionally, the most senior roles that lead product teams or oversee the development of basic and large-scale language models are increasingly going to academics and industry stalwarts who previously pursued work in the field of research or for more academic purposes. This is especially true in healthcare, which requires complex industrial knowledge to apply to the fundamentals of AI.
For example, Isomorphic laboratories And Google DeepMindtwo Alphabet companies, have worked for many years at the intersection of healthcare, technology and biology. With this work, the latest iteration of the AlphaFold A tool was recently announced that leverages the best of machine learning and advanced baseline models to advance the fields of proteomics, chemistry, and biology. While this may seem like an immediate success, the work that went into developing this innovation took years, if not decades, and originated in research labs. Consequently, the direction behind much of the work congruently reflects a highly academic and research-oriented pedigree.
Another example is Apple and it’s work in the health field. Although Apple is becoming a recognized titan in the field of artificial intelligence with its line of products aimed at consumer healthcare, the company has for many years relied on the talent of clinicians and healthcare experts to help develop technology and workflows. Now, with this basic knowledge, the means to carry out this fundamental work and integrate it with usable artificial intelligence, models and products for consumers requires seasoned experts, from both academic and commercial backgrounds, to ultimately convert ideas into viable products.
So, despite the growth and increase in demand, companies remain incredibly selective in hiring candidates to work in this field, making it one of the most competitive career fields today. Without the right constellation of research, experience, and specialized skills in the field, it is almost impossible to get a job in the field of AI. In fact, “research shows that the majority of business leaders (66%) will not consider hiring a candidate who does not have AI skills… 71% of executives say they would prefer to hire a candidate with AI skills, even if they have AI skills. less experience, compared to a more experienced candidate who has no aptitude for AI.
At the rate these fields are growing, the future is very bright when it comes to AI and ML jobs. Educators, innovators and institutions will need to be ready to meet the demands and needs of a modern workforce.