AI applications in healthcare: Is it just a FOMO thing?

As a subfield of computer science, Artificial Intelligence (AI) encompasses hardware and software that can perceive its environment and take action that maximizes its chances of successfully achieving its goals. In 2019, Morgan Stanley estimated that the global market for AI in health care could surge from $1.3 billion to $10 billion by 2024, growing at an annual compound rate of 40%. In November 2022, an AI system named Generative Pre-trained Transformer (GPT) was released to the public. This release highlighted many practical examples of ethical issues to be considered in health care, so frequent references to that system will be made. In AI terms, one might even be tempted to consider a pre-ChatGPT world vs. a post-ChatGPT world. Still, it must be remembered that the major significant difference was its public release and accessibility rather than its development.

Artificial intelligence is the simulation of human intelligence in machines programmed to think like humans and mimic their actions, such as learning and problem-solving. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is Machine Learning, which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning by absorbing vast amounts of unstructured data such as text, images, or video.AI is still used for solving problems in which the possible outcomes are limited, computational power is scarce, or human explainability is essential. However, in healthcare, where issues are complex, not always fully understood, and have many explanatory variables, building a model based on a limited set of rules is extremely difficult, if not impossible.

The discussion on AI applications often raises one pertinent question that stimulates a lot of skepticism, “Will AI replace human clinicians?” In an evidence-based review, Jayne Kessel, a first-year resident student at the University of Iowa, opines, “Overall, it appears that using AI to the treatment plan may never be able to give a definitive answer, not because AI is ineffective, but because treatment planning is always subjective.” This observation reflects a great understanding of the nuances involved in orthodontic decision-making. No current tools exist to lead patients and clinicians out of the decision-making uncertainty. Clinicians will always face a condition with several possible correct treatment options, although some are better. In this context, artificial intelligence (AI) can make significant strides! 

Can we ever automate every job out of existence? Human beings are relentless in their capacity to invent new ways to serve, so that question seems addressed vehemently. A recent McKinsey study shows, “There really are tasks that only humans can do for the foreseeable future, particularly in areas such as education and healthcare; there are still aspects of many jobs that cannot be automated.” As the readers of this paper take a deep breath, it’s important to understand another phenomenon. As this next wave of automation transpires, it will unveil the real value of human beings at work. Humans understand the dynamic nuances inherent in working relationships and the ability to respond and adapt to those nuances; machines probably will not get there. This is where strong mentors excel. Humans can provide judgment and hold each other accountable for their actions. A single fatal accident with a self-driving car reminds us that machines, for all their excellent attributes, cannot be blamed or accept responsibility. 

The successful professionals of tomorrow must be well prepared to function in a technological and automated work environment. With the right technology skills and automation in play, we are learning that physician assistants and paralegals can do the same work with lesser investments in human resources. These early predictors of change aren’t negative, but people concerned about how the automation of cognitive work might impact their careers can’t stick their heads in the sand. If we continue to innovate and incorporate technological skills into our education systems, we will continue to create career opportunities that only humans can effectively seize.

In striving towards an “AI-driven orthodontic utopia,” another pertinent issue needs to be addressed, i.e., setting algorithm standards. Probably an evaluation similar to an “Algorithm Board” examination! The logical trail and the learning corpus that lead to machine decisions need to be made available for examination and peer review. A set of technological and evidence-based parameters, with best-practices benchmarks, must be established to evaluate them. Most AI experts I interact with think we will need to set higher standards for artificial intelligence than humans. We do not, as a matter of course, expect people to supply a list of influences and account for their logic for every decision they make unless something goes horribly wrong. But we will require such transparency from machines. “With another human, we often assume that they have similar common-sense reasoning capabilities and ethical standards. That’s not true of machines, so we need to hold them to a higher standard.” The legal implications of AI-based clinical decisions should also be considered before implementation.

As pervasive as artificial intelligence is likely to become soon, the responsibility of setting standards and protocols rests with educators, clinicians, regulators, and leaders of the profession. We need to create and implement the standards by which artificial intelligence will operate just as seriously as those that govern how our political systems work and how our children are educated. This seamless synergy between “the old morality and the new economy” is critical to our survival and future. Therefore, the emotion that needs redressal is not the fear of missing out (FOMO) but "what system checks and balances are we exactly missing out on?”

 

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