According to a study by Statista, the global Artificial Intelligence (AI) market is set to grow up to 54 percent every single year.

While the whole world seems to be going bananas about AI, there’s a need to acknowledge that AI can’t be either a complete blessing or a curse.  Perhaps the critical success factor should be to strike a balance with the intention to develop and implement workable AI solutions that would empower businesses and governments around the world with the focus to derive the maximum benefits.

Nevertheless, like anything else, the success of AI is totally dependent upon who, what, where, when, and why AI solutions are developed and implemented.

Here’s how IBM defined AI:

  • Artificial Intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

It all began with computer scientist, and cryptanalyst Alan Turing’s 1950 paper on computer intelligence. In it he outlined his now famous approach to identifying machine intelligence, known as the Turning Test or Imitation Game. Turing described a three-player game in which a human “Interrogator” is asked to communicate via text with another human and a machine and judge who composed each response. If the interrogator cannot reliably identify the human, then according to Turing, the machine can be said to be intelligent

As a background, computers evolved rapidly and quickly became integral parts of peoples’ daily lives.  The first digital computers were invented only 80 years ago, as the following diagram illustrates the timeline:

The second timeline reflects when the AI systems were built. Here’s an explanation:

  1. Theseus: First built in 1950, it was one of the world’s first examples of machine learning: A robotic maze-solving mouse known as Theseus;
  2. Perceptron Mark1: First built in Mid 1950s, The Mark I Perceptron is a pattern learning and recognition device. It can learn to classify plane patterns into groups on the basis of certain geometric similarities and differences;
  3. TD-Gammon: First built in mid 1990s. Based on the reinforcement learning algorithm, TD-Gammon is a neural network that can teach itself how to play backgammon by playing against itself and learning from the results; and
  4. AI with:
    • Language: First built in 2020, Language AI, or natural language processing (NLP), is a branch of AI that focuses on enabling computers to understand, interpret, and generate human language; and
    • Image Recognition: First built in 2020, image recognition can speed up tedious tasks and process images faster or more accurately than manual image inspection. Image recognition is a crucial technique in many applications, and is the main driver in deep learning applications.

Researchers have outlined four types of Artificial Intelligence machines/computers:

  1. Reactive Machines: Reactive machines are the most basic type of artificial intelligence. Machines built in this way don’t possess any knowledge of previous events but instead only “react” to what is before them in a given moment. As a result, they can only perform certain advanced tasks within a very narrow scope, such as playing chess, and are incapable of performing tasks outside of their limited context;
  2. Limited Memory Machines: Machines with limited memory possess a limited understanding of past events. They can interact more with the world around them than reactive machines can. For example, self-driving cars use a form of limited memory to make turns, observe approaching vehicles, and adjust their speed. However, machines with only limited memory cannot form a complete understanding of the world because their recall of past events is limited and only used in a narrow band of time;
  3. Theory of Mind: Machines that possess a “theory of mind” represent an early form of artificial general intelligence. In addition to being able to create representations of the world, machines of this type would also have an understanding of other entities that exist within the world. As of this moment, this reality has still not materialized; and
  4. Self-Aware Machines:  Machines with self-awareness are the theoretically most advanced type of AI and would possess an understanding of the world, others, and itself. This is what most people mean when they talk about achieving AGI. Currently, this is a far-off reality. 

Here’s an image which illustrates the configuration of AI branches (

The following three charts illustrate the advantages and disadvantages of AI in five distinctive sectors – 1. Healthcare; 2. Marketing; 3. Education; 4. Creativity; and 5. Transport:

It’s true that AI in the context of Alexa, Siri or Self-Driving cars is most popular whereas the potential for the clinical applications of AI is unhindered.  The steady rise in the impact of AI in healthcare can be exemplified by looking at the following five sectors of the healthcare industry.  The applications and examples of Artificial Intelligence in healthcare hold the promise of affordable healthcare, improved success rates, efficient clinical trials, and a better quality of life. 

Details can be accessed about the following AI tools by clicking each listed below:

  1. Artificial Intelligence Assistance;
  2. AI-assisted robotic surgery
  3. Clinical judgement or diagnosis
  4. Precision medicine
  5. Drug discovery

Here’s a reality check: When a local retired physician who was known for being compassionate and scrupulous to her patients throughout her practice in Canada, was asked: How did she like working with the AI tools which were at her disposal while working in the hospital?  She said:

  • It certainly has been a benefit when it is enabling access to surgeons and other specialists in remote locations, and for surgeons in those places to perform surgeries under supervision from major centres;
  • I still feel, however, that its unlimited use in hospital medicine and general practice has allowed lazy, greedy physicians to work less, for shorter hours and more remuneration, with far less patient interaction and satisfaction; and
  • As a result, I believe that patients are not getting the medical care that they deserve and have come to expect.

She continued saying that unfortunately I am a dying breed, and when all the “Old” physicians have retired or died, there will be a new, much lower level of care accepted as normal. I’m glad and privileged to have practised medicine when I did, and with the training I received. We were always trained to have the skills to practise wherever we found ourselves. Can you imagine how any of our young physicians would cope if they were to be dropped in a remote area with no internet access? The thought is disturbing.                                                  

It is indeed a sad statement of fact.  It could be considered a “curse”, but the technology itself cannot be blamed for the mismanagement and incompetence of individual practitioners who are misguided by excessive use of AI tools in hospital medicine and general practice just to make money.

An eyeopener; Technological change is inherently disruptive and entails difficult transitions. It also inevitably creates winners and losers.  Unfortunately, the health organizations around the world that are responsible for implementing AI tools have been slow to adapt to the challenges of change. With improved and more responsive policies, better outcomes are feasible.

An example; adoption of AI in the Canadian health sector has lagged behind other countries and sectors.  To help speed up greater AI adoption, Healthcare Excellence Canada (HEC), formerly the Canadian Foundation for Healthcare Improvement and the Canadian Patient Safety Institute, partnered with an advisory committee of experts to develop strategies to support health leaders in AI implementation.

While there is recognition of the benefits associated with AI tools and techniques in the field of medicine which could be considered blessings, the fact remains that right now patients are not getting the medical care they deserve and have come to expect.  Unfortunately, this cannot be just an isolated example only in Canada. Accordingly, the anticipated “New Normal” means that in the future patients will be left with no choice but to accept a lower level of care, which indeed would be nothing but a curse. 

Greely, Ontario, Canada 24 January 2024

Greely, Ontario, Canada            24 January 2024