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In just a few months, artificial intelligence (AI) has certainly exploded onto the stage in a way that has surprised many. Take, for instance, the mass popularity of Chat GPT, GPT-3, GPT-2, and BERT. The scale and intelligence, with the advancement of computing power with large data sets, provide fertile ground for AI to take off.1,2

For us in medicine, we are used to applying approaches to diagnosis and treatment that are rooted in deep understanding of disease processes and informed by critical appraisal of evidence-based strategies and experience over time. Medicine has adapted and kept pace with the various emerging technologies and, as a field, has reached many advances.3 Part of the heuristic and epistemological approach is that technology has always been a tool to be applied to the medical process.4

Agency and control have been at the forefront of how we use tools. However, with the introduction of tools, there was some initial trepidation. When one looks, for example, at the evolution of different tools over time, in some ways, every tool has brought on some initial anxiety and fear. One can only imagine the angst of a painter with the emergence of photography, and yet, painting and art have not been displaced.

The emergence of AI has generated much for even those embedded in the technological field. An approach to machine learning and artificial control intelligence should probably stem from an understanding of what it is and what it can do. In taking this approach, we are positioning ourselves in a way to inform industry and help solve problems that are meaningful with an ethical and value-based framework.

The emergence of technology and its adoption in society has brought on various emotions in its adaptation. A number of researchers have explored this area . One particular Model is Gartner's Hype Cycle, whereby new technologies are followed by an up-peak of excitement, followed by a disillusionment phase, and then a normalization phase where one understands the utility and limitations of the new tool.

Another heuristic to understand emerging technology is through an economic perspective. The Kondratiev Wave theory describes economic cycles in the economy and links them with technology. Another researcher in the field of paradigm shifts, Carlota Perez, defines technological revolution as a powerful and highly visible cluster of new and dynamic technologies, products, and industries capable of bringing about an upheaval in the whole fabric of the economy and propelling a long-term upsurge of development.

It is quite astounding that a machine can read large amounts of data and emulate and identify patterns, but, at its heart, not quite understand what it is doing. So, although technology can incorporate an immense amount of knowledge that is often cultivated over many years in a rapid time, it still has challenges with reasoning.

For us in the medical world, it is hard to imagine a system that emulates what we do: Refine the diagnostic process and apply knowledge to patterns based on genetics, epigenetics, life experiences, and responses to various medication therapies, and then fine-tune this to each patient while seeing it from the individuals perspectives and values.

So, one may ask, what is the concern? A recent letter from several technology leaders spoke to the concerns around the rapid deployment of AI.5

In some ways, these technological innovations have always had human beings behind the controls. What is currently challenging and concerning for various individuals, including those in the fields of computer science and engineering, though, is the lack of clarity with which the machine itself can reason and the risk that this can pose. However, although the genie is out of the lamp, we can try to position ourselves at the front and center of the decision-making process and help inform innovators, inventors, and data scientists.

Much of the machine learning model is based on teaching the machine how to learn and reason, drawing from a number of mathematical models. In order to understand the underlying AI technology, it is helpful to take a closer look at how AI models are structured.

Machine Learning Models: Recurrence and Convolution Transformers

Recurrence and convolution transformers are 2 important concepts in AI that have been widely used in machine learning models. Recurrence helps models remember what happened before, whereas convolution finds important patterns in data and transformers focus on understanding relationships between different parts of the input.

Recurrence

Think of recurrence as a memory that helps a model remember information from previous steps. It is useful when dealing with things that happen in a specific order or over time. For example, if you are predicting the next word in a sentence, recurrence helps the model understand the words that came before it. It is like connecting the dots by looking at what happened before to make sense of what comes next.

Convolution

Convolution is like a filter that helps the model find important patterns in data. It is commonly used for tasks involving images or grids of data. Just like our brain focuses on specific parts of an image to understand it, convolution helps the model focus on important details. It looks for features like edges, shapes, and textures, allowing the model to recognize objects or understand the structure of the data.

Transformers

Transformers are like smart attention machines. They excel in understanding relationships between different parts of a sentence or data without needing to process them in order. They can find connections between words that are far apart from each other. Transformers are especially powerful in tasks like language translation, where understanding the context of each word is crucial. They work by paying attention to different words and weighing their importance based on their relationships.

How Transformers Became So Impactful

A landmark 2017 paper on AI titled, Attention Is All You Need by Vaswani and colleagues6 laid important work in understanding the transformer model. Unlike recurrence and convolution, the transformer model relies heavily on the self-attention mechanism. Self-attention allows the model to focus on different parts of the input sequence during processing, enabling it to capture long-range dependencies effectively. Attention mechanisms allow the model-to-model dependencies between input and output sequences without considering their distance. This allows the machine incredible advanced capabilities, especially when powered with advanced computing power.

Machine Learning Frameworks

Currently, there are several frameworks that can be applied to the machine learning process:

The CRISP-DM approach involves about 8 phases:

Concerns With AI

In medicine and psychiatry, we are familiar with distortions that can arise in human thinking. We know that thinking about what we are thinking about becomes an important skill in training the mind. In AI, the loss of human control and input in informing the machines is at the heart of many concerns. There are several reasons for this.

Addressing these concerns requires a comprehensive approach that emphasizes transparency, accountability, fairness, and human oversight in the development and deployment of AI systems. It is crucial to consider the societal impact of AI and to establish regulations and guidelines that ensure its responsible and ethical use.

Positives and Negatives in the Medical Community

For the medical community specifically, this new technology brings both positives and negatives. By leveraging the potential of AI while addressing its limitations and concerns, health care can benefit from improved diagnostics.

Positive aspects:

Negative aspects:

Evaluating AI Technology

A proposed mechanism for physicians and health care workers to evaluate technology might be a framework similar to what we have identified as an evidence-based tool. Here are some guiding questions for evaluating the technology:

A couple of suggested evaluation tools that can be used in interpreting AI models in health care are listed in Figures 1 and 2. These mnemonics can serve as a framework for health care professionals to systematically evaluate and interpret AI models, ensuring that ethical considerations, transparency, and accuracy are prioritized in the implementation and use of AI in health care.

Dr Amaladoss is a clinical assistant professor in the Department of Psychiatry and Behavioral Neurosciences at McMaster University. He is a clinicianscientistand educator who has been a recipientof anumberof teaching awards. His current research involves personalized medicine and theintersection of medicine and emerging technologies including developing machine learning models and AI in improving health care. Dr Amaladoss has also been involved with the recent task force on AI and emerging digital technologies at the Royal College of Physicians and Surgeons.

Dr Ahmed is an internal medicine resident at the University of Toronto. He has led and published research projects in multiple domains including evidence-based medicine, medical education, and cardiology.

References

1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence.Nat Med. 2019;25(1):44-56.

2. Szolovits P. Ed. Artificial Intelligence in Medicine. Routledge; 1982.

3. London AJ. Artificial intelligence in medicine: overcoming or recapitulating structural challenges to improving patient care?Cell Rep Med. 2022;3(5):100622.

4. Larentzakis A, Lygeros N. Artificial intelligence (AI) in medicine as a strategic valuable tool.Pan Afr Med J. 2021;38:184.

5. Mohammad L, Jarenwattananon P, Summers J. An open letter signed by tech leaders, researchers proposes delaying AI development. NPR. March 29, 2023. Accessed August 1, 2023. https://www.npr.org/2023/03/29/1166891536/an-open-letter-signed-by-tech-leaders-researchers-proposes-delaying-ai-developme

6. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. NIPS. June 12, 2017. Accessed August 10, 2023. https://www.semanticscholar.org/paper/Attention-is-All-you-Need-Vaswani-Shazeer/204e3073870fae3d05bcbc2f6a8e263d9b72e776

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Attention to Attention is What You Need: Artificial Intelligence and ... - Psychiatric Times