AI/ML Features and Their Relationship to Complex Adaptive Systems and Primary Care
| Complexity in Primary Care | AI/ML/Big Data | |
|---|---|---|
| Philosophy | A complex system is one in which there are so many interacting parts that it is difficult to predict the behavior of the system based on knowledge of its component parts. People are the glue that binds and maintains the system.7 Complex systems have chaotic and non-linear complex parts as well as linear and complicated features. | Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans. No established unifying theory or paradigm has guided AI research for most of its history. |
| The Challenge | The General Systems Theory (GST)8 is studying the whole system, to clarify the principles that can be applied to all types of systems at all levels of their nesting in the other systems in terms of their viability in all subject areas of research, given their interaction with each other in real-time and in fuzzy environment, surrounding them.9 How to inform the human in the loop with intelligent reliable information that is relevant? | Develop intelligent systems thinking in an autonomous fuzzy control, operate in fuzzy environments under uncertainty, and communicate with humans and other systems in different languages, in the dissimilar domains, where processes, situations and factors of influence on the control object and back; a) cannot be determinate and structured in advance, and b) may be understood relevantly and unambiguously.9–11 |
| Responding to the dynamical systems that vary over time. | The human brain can be understood as a complex adaptive system itself that have evolved to enable humans to navigate complex environments12 and dynamical systems. Exploration is ongoing in attempts to understand, emotion, context and human reasoning.13,14 | Dynamical systems obey differential equations involving time derivatives. Analytical resolution of such equations or their integration over time through computer simulation may facilitate the prediction of the future behavior of the system. |
| Intelligence | Human Intelligence aims to adapt to new environments by utilizing a combination of different cognitive processes. The human brain is analogous and uses its computing power to recognize multiple patterns, diverse memories, interoception14,15 and ability to think to make sense16 of their environment and make decisions. | Artificial Intelligence aims to build machines that can mimic human behavior and perform human-like actions. AI-powered machines rely on data and specific instructions fed into the system. They have perfect memory but the memory is constrained by their capacity to analyze and infer from human and other inputs. |
| Environmental and sensory Inputs - theory | Human consciousness in an adaptation to a new environmental disturbance. Through the conversion of neural cognitive activity - thoughts - about the state of the outside world into the way that world is experienced through the senses, the thoughts gain the reality that sensory images have.17 | Human intelligence (explicit) is the main contributing factor that has given definition to the simulations that are created in machine intelligence. Artificial intelligence depends on the best current theoretical models, input data, and the constraints of the AI machine problem-solving skills. |
| Strengths | Human cognition has evolved to adapt to our changing world and navigate our environments.18 Garnering both explicit and implicit knowledge, exhibits the highest degree of evolutionary cortical expansion, supported by receptor diversity and human-accelerated genes underpinning synaptic function. | Artificial machine learning is a development from human cognition to address weakness in human cognition – access to information, memory, processing time, etc. Machines are better than humans at processing large amounts of data. This will be most useful in the simple and complicated domains of health care knowledge. |
| Weaknesses | Humans have limited explicit memory (e.g., cannot easily use the whole of PubMed on a topic) compared with machine intelligence; Humans have as a group a wide spectrum of intellectual capabilities; however these are influenced by stress, unsupportive environments, distractions, lack of access to a vast body of knowledge that is exponentially growing every day. | AI is still in its early stages of development. Training AI systems is a time-consuming process. AI will have all the biases and limitations of those who wish to ensure top-down control via protocol vs guidance. |
| Challenges | Many tasks are time consuming. Workforce and remuneration challenges undermine personal communication, interoception, and person-centered care. How to self-organize bottom-up care when practice is driven by top-down constraints and funding formulae? | Top-down medical industrial for-profit drivers of the artificial data and information systems may come to dominate the primary care space without truly understanding the human and environmental dynamics. |
| How to self-organize and adapt in the complex and even chaotic domains of practice? | The information system should serve rather than dominate clinical care. |