By Daniel Cabrera (@CabreraERDR)
(If you’re interested in complex adaptive systems and / or stigmergy.. or if you want to understand what that means to make sense of this post, check out part 1 here)
Within medical education there is a (distant) move from an individual competency model to a collective competence construct, where the outcomes of education and healthcare are not defined by the isolated performance of an individual but by the complex interconnections of multiple agents. At the same time, we need to start considering how to incorporate collective clinical competence with dataism and artificial intelligence (AI).
The idea of collective competence, developed by Lorelei Lindgard and Bryan Hodges, mirrors the concepts of stigmergy and rhizomatic organization of networks. Traditionally we have focused on hyper-specialization, data reductionism and individual performance. However, a more decentralized architecture calls for multipotentiality, contextuality, interconnection, data augmentation and network/community performance. As Lindgard proposes, this can be mainly achieved with technological affordances and constraints.
We have that technological affordances and constraints now. We are witnesses to the arrival of soft Artificial Intelligence (AI) in our lives, from preemptive recommendations on what we want to buy to predictions of who is pregnant. This type of AI is becoming ubiquitous in our clinical practice, particularly in the domains of pharmacotherapeutics and decision support. Currently, decision support is nothing more than a cognitive crutch, but it is becoming increasing intrusive in all aspects of clinical care. As clinicians and educators, we have not given enough attention to the issue of how interact with soft AI. (e.g., If pharmacy decision supports become universal, why should new learners know anything about it? Can we shorten training? Can we just focus on diagnosis and decision-making? ) I feel that this particular train has already left the station and many of our learners are using these tools without understanding the key concepts behind them.
Although soft AI is creating a lot of questions, the tectonic change will come with the advent of strong AI. This event, the emergence of efficient, supra human and massive data management intelligence will redefine what we do. The day that a strong AI tell us that our diagnosis is wrong and our treatment recommendations are faulty is not far into the future; I’m certain this will happen during my life time.
We need to start thinking and planning our roles for a future where AI will take most of the important decisions with little input from humans. Will the training of future doctors
Many experts think that data can’t self organize (following Claude’s entropy). However, strong AI will almost certainly behave in a way that assures instrumental goal achievement, self-preservation and resource acquisition. Strong AI will relentlessly pursue the objective that they are programmed for even if is not aligned with human priorities. We have to be very careful in deciding and programming what those goals are.
We are entering the age of dataism, where authority and truth does not emanate from human self-determination, but from data analysis. If we don’t pay attention to the changes around us, we are threaten to become nothing more than biological data entry agents to a supra human mind; we will become the machine of the Ghost in the Machine problem. The ultimate challenge is to create a framework for strong AI that guarantees that the prime directive of the system is to achieve what is good for the patient, what is good for the patient’s life, what is good by the patient’s self-determination and value structure and not necessarily good according to the AIs optimal solution. As educators, we need to start thinking about how to teach these digital beings about what it is to be human, how medicine is about helping, comforting and accompanying our patients, and not only the optimization of diagnosis and treatment. Finally, we need to start thinking about how we are going to learn from non-human teachers.
References and further reading
- La Innovacion Pendiente. Cobo C. [Spanish].
- Yuval Noah Harari on big data, Google and the end of free will. Harari YN. Financial Times.
- What is an evolutionary algorithm? In: Introduction to Evolutionary Computing. Eiben AE and Smith JE.
- Paradoxical Truths and Persistent Myths: Reframing the Team Competence Conversation. Lingard L
- Paths, dangers, strategies. Bostrom N. Oxford University Press.
- The Ghost in the Machine. Koestler A.
- “Answer”. Brown F.
“Answer” by Fredric Brown (1954)
Dwan Ev ceremoniously soldered the final connection with gold. The eyes of a dozen television cameras watched him and the subether bore throughout the universe a dozen pictures of what he was doing.
He straightened and nodded to Dwar Reyn, then moved to a position beside the switch that would complete the contact when he threw it. The switch that would connect, all at once, all of the monster computing machines of all the populated planets in the universe — ninety-six billion planets — into the supercircuit that would connect them all into one supercalculator, one cybernetics machine that would combine all the knowledge of all the galaxies.
Dwar Reyn spoke briefly to the watching and listening trillions. Then after a moment’s silence he said, “Now, Dwar Ev.”
Dwar Ev threw the switch. There was a mighty hum, the surge of power from ninety-six
billion planets. Lights flashed and quieted along the miles-long panel.
Dwar Ev stepped back and drew a deep breath. “The honor of asking the first question is yours, Dwar Reyn.”
“Thank you,” said Dwar Reyn. “It shall be a question which no single cybernetics machine has been able to answer.”
He turned to face the machine. “Is there a God?”
The mighty voice answered without hesitation, without the clicking of a single relay.
“Yes, now there is a God.”
Sudden fear flashed on the face of Dwar Ev. He leaped to grab the switch.
A bolt of lightning from the cloudless sky struck him down and fused the switch shut.
Image 2 courtesy of Dalle Molle Institute for Artificial Intelligence Research, via Wikimedia Commons