Some experts have suggested that healthcare may be the industry most impacted by the rise of AI and related technologies. Therefore earning a degree related to AI in the healthcare field promises to open the door to an array of career options. To gain insight into current career opportunities, and what they may be in the future, it is important to understand both recent trends and future prospects of using AI in the healthcare field.
The Role of AI in Healthcare
Many in the healthcare field believe that AI has the ability to improve the delivery and results of healthcare by enhancing, rather than replacing, the work performed by healthcare professionals. AI has application as a means of improving clinical workflows and increasing the accuracy of administrative duties, as well as performing more specialized tasks such as analyzing medical images and automating medical devices.
Demand for healthcare services, and the expectations of patients receiving them, are increasing. At the same time, many markets are experiencing a shortage of healthcare workers, and the costs of providing some healthcare services are increasing. Converging these trends has created a very strong interest in the development and application of AI technology related to healthcare.
Many view cost considerations as a primary impetus for developing healthcare-relevant AI. Cost saving potential from integrating AI in U.S. healthcare is estimated at $150 million annually, partly due to the expectation that AI can play a crucial role in transitioning from reactive to proactive healthcare delivery. Such expectations have incentivized tech companies to innovate AI products for use in healthcare settings.
Innovation in healthcare-relevant AI has already created a significant need for people with the education and skills necessary to implement these new technologies. As AI continues to impact the healthcare sector, this need can only be expected to burgeon.
Growth in the AI-Healthcare Sector
Growth of AI in the healthcare market has been rapid and is expected to continue. For example, a 2017 market analysis projected more than 40% growth in the healthcare-relevant AI market, resulting in an anticipated AI-associated market of over $2.5 billion in medical imaging and diagnostics alone (and over $10 billion overall) by 2024. Such predictions made prior to the COVID-19 pandemic were not accurate; they were, in fact, too low.
The COVID pandemic accelerated the growth of AI adoption in the healthcare market. More than in other industries, the pandemic significantly increased healthcare and pharmaceutical companies’ investments in AI technologies. In fact, AI technologies played a role in the rapid development of a vaccine to combat COVID-19.
Uses of AI in Healthcare
The explosive growth of AI technologies in the healthcare field and the increasing investment in their development begs the question: How is AI actually used in the healthcare industry?
Although experts vary in their predictions of what specific AI applications will become most important in the healthcare sector, there are several broad categories in which AI improves the quality and/or delivery of healthcare. These include
- increasing the precision and effectiveness of treatments based on individual or group characteristics;
- enhancing the use of medical imaging in diagnosis, interventions, and training;
- improving personal health records through increased data points and accuracy, as well as through integration among providers; and
- developing the potential of robotics, such as through minimally invasive surgeries, more responsive prosthetics, and various forms of physical and mental assistance in patients’ daily lives.
Each of these broad categories includes numerous more specialized healthcare uses of AI that currently exist, as well as many potential future uses as AI adoption continues. For example, more precise healthcare through AI involves research into genetics-based diagnostic improvements made possible by AI’s ability to analyze very large data sets utilizing complex algorithms. Likewise, the capabilities of AI enable more rapid drug development because of the highly complex chemical testing necessary to understand which compounds may offer potential benefit.
Many of the uses of AI in healthcare highlighted above depend on machine learning. In essence, AI offers important opportunities for development through complex medical algorithms that may ultimately replace many of the rule-based expert systems that have dominated medicine historically (illustrated by “if . . . then” reasoning in diagnosis, for example).
Machine learning, particularly in the form of deep learning, allows for the assessment and understanding of very large data sets in ways that have not been possible with human cognition alone. Although highly complex in their operation, deep learning AI technologies are now increasingly relevant to patients and healthcare practitioners due to the advent of natural language processing technologies, which enable machine learning from input of familiar and commonly used human language (rather than complex programming code, for example). In this and related ways, many believe that AI technologies will truly revolutionize healthcare in coming years.
While perhaps more mundane, AI technologies that facilitate the administration of healthcare systems have widespread application and value. Beyond managing patient records, tasks such as documenting care or processing claims are becoming far more efficient with the assistance of AI technologies.
Ethical Considerations and Risks of AI Adoption in Healthcare
Most informed discussions of the current and potential uses of AI in healthcare emphasize the probability of tremendous increases in the quality and efficiency of care as AI technologies advance and become more integrated into healthcare delivery systems. Even so, the changes brought by AI in the healthcare sector will, at a minimum, spawn new ethical questions and new pathways of risk.
Perhaps the biggest potential negative effects of AI adoption in healthcare are those related to the increased ability to collect and retain data. This ability opens up new risks to privacy and may create new pathways for malicious actors to threaten security. A similar threat may emerge if malicious actors are able to “fool” AI technologies in the healthcare setting and thereby cause potentially life-threatening errors.
Ultimately, the most difficult questions regarding AI in healthcare may prove to be the ethical ones. AI does not operate in a vacuum but depends on humans to develop and operate the technologies, resulting in a very real risk that AI technologies may reflect social and individual biases, even if only unintentionally. This particular risk of AI may prove especially difficult to assess in advance because it’s not clear that those with the ethical knowledge to assess the risk have the technical expertise to understand how to apply their training in this new field. Similarly, many with the technical training to develop and utilize AI may lack a full understanding of the social risks created by unconscious biases or similar forces that may color perception and affect AI.
AI in healthcare is here to stay, will continue to grow for the foreseeable future, and has the potential to literally revolutionize the healthcare systems we currently know. The adoption of AI in the industry has accelerated over recent years, which reinforces the increasing pace of investment and development in these technologies.