a proposal for the dartmouth summer research project on artificial intelligence doi

A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence

The proposal for the Dartmouth Summer Research Project on Artificial Intelligence was, so far as I know, the first use of phrase Artificial Intelligence. If you are interested in the exact typography, you will have to consult a paper copy.

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A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955

John mccarthy , marvin minsky , n rochester , claude e. shannon.

A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955

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A Look Back on the Dartmouth Summer Research Project on Artificial Intelligence

At this convention that took place on campus in the summer of 1956, the term “artificial intelligence” was coined by scientists..

For six weeks in the summer of 1956, a group of scientists convened on Dartmouth’s campus for the Dartmouth Summer Research Project on Artificial Intelligence. It was at this meeting that the term “artificial intelligence,” was coined. Decades later, artificial intelligence has made significant advancements. While the recent onset of programs like ChatGPT are changing the artificial intelligence landscape once again, The Dartmouth investigates the history of artificial intelligence on campus.   

That initial conference in 1956 paved the way for the future of artificial intelligence in academia, according to Cade Metz, author of the book “Genius Makers: the Mavericks who Brought AI to Google, Facebook and the World.” 

“It set the goals for this field,” Metz said. “The way we think about the technology is because of the way it was framed at that conference.”

However, the connection between Dartmouth and the birth of AI is not very well-known, according to some students. DALI Lab outreach chair and developer Jason Pak ’24 said that he had heard of the conference, but that he didn’t think it was widely discussed in the computer science department. 

“In general, a lot of CS students don’t know a lot about the history of AI at Dartmouth,” Pak said. “When I’m taking CS classes, it is not something that I’m actively thinking about.”

Even though the connection between Dartmouth and the birth of artificial intelligence is not widely known on campus today, the conference’s influence on academic research in AI was far-reaching, Metz said. In fact, four of the conference participants built three of the largest and most influential AI labs at other universities across the country, shifting the nexus of AI research away from Dartmouth.

Conference participants John McCarthy and Marvin Minsky would establish AI labs at Stanford and MIT, respectively, while two other participants, Alan Newell and Hebert Simon, built an AI lab at Carnegie Mellon. Taken together, the labs at MIT, Stanford and Carnegie Mellon drove AI research for decades, Metz said.

Although the conference participants were optimistic, in the following decades, they would not achieve many of the achievements they believed would be possible with AI. Some participants in the conference, for example, believed that a computer would be able to beat any human in chess within just a decade. 

“The goal was to build a machine that could do what the human brain could do,” Metz said. “Generally speaking, they didn’t think [the development of AI] would take that long.”

The conference mostly consisted of brainstorming ideas about how AI should work. However, “there was very little written record” of the conference, according to computer science professor emeritus Thomas Kurtz, in an interview that is part of the Rauner Special Collections archives. 

The conference represented all kinds of disciplines coming together, Metz said. At that point, AI was a field at the intersection of computer science and psychology and it had overlaps with other emerging disciplines, such as neuroscience, he added. 

Metz said that after the conference, two camps of AI research emerged. One camp believed in what is called neural networks, mathematical systems that learn skills by analyzing data. The idea of neural networks was based on the concept that machines can learn like the human brain, creating new connections and growing over time by responding to real-world input data.

Some of the conference participants would go on to argue that it wasn’t possible for machines to learn on their own. Instead, they believed in what is called “symbolic AI.” 

“They felt like you had to build AI rule-by-rule,” Metz  said. “You had to define intelligence yourself; you had to — rule-by-rule, line-by-line — define how intelligence would work.”

Notably, conference participant Marvin Minsky would go on to cast doubt on the neural network idea, particularly after the 1969 publication of “Perceptrons,” co-authored by Minsky and mathematician Seymour Paper, which Metz said led to a decline in neural network research.

Over the decades, Minsky adapted his ideas about neural networks, according to Joseph Rosen, a surgery professor at Dartmouth Hitchcock Medical Center. Rosen first met Minsky in 1989 and remained a close friend of his until Minsky’s death in 2016.

Minsky’s views on neural networks were complex, Rosen said, but his interest in studying AI was driven by a desire to understand human intelligence and how it worked.

“Marvin was most interested in how computers and AI could help us better understand ourselves,” Rosen said.

In about 2010, however, the neural network idea “was proven to be the way forward,” Metz said. Neural networks allow artificial intelligence programs to learn tasks on their own, which has driven a current boom in AI research, he added.

Given the boom in research activity around neural networks, some Dartmouth students feel like there is an opportunity for growth in AI-related courses and research opportunities. According to Pak, currently, the computer science department mostly focuses on research areas other than AI. Of the 64 general computer science courses offered every year, only two are related to AI, according to the computer science department website. 

“A lot of our interests are shaped by the classes we take,” Pak said. “There is definitely room for more growth in AI-related courses.”

There is a high demand for classes related to AI, according to Pak. Despite being a computer science and music double major, he said he could not get into a course called MUS 14.05: “Music and Artificial Intelligence” because of the demand.

DALI Lab developer and former development lead Samiha Datta ’23 said that she is doing her senior thesis on neural language processing, a subfield of AI and machine learning. Datta said that the conference is pretty well-referenced, but she believes that many students do not know much about the specifics.

She added she thinks the department is aware of and trying to improve the lack of courses taught directly related to AI, and that it is “more possible” to do AI research at Dartmouth now than it would have been a few years ago, due to the recent onboarding of four new professors who do AI research. 

“I feel lucky to be doing research on AI at the same place where the term was coined,” Datta said.

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• Published: 10 November 2020

Materializing artificial intelligence

Nature Machine Intelligence volume  2 ,  page 653 ( 2020 ) Cite this article

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Artificial intelligence can be defined as intelligence demonstrated by machines. But what counts as intelligence, and how intelligence is implemented in different kinds of machines, robots and software varies across disciplines and over time.

A growing group of researchers believes that the ‘intelligence’ in what we currently call artificial intelligence (AI) is limited: it is on the one hand too attached to data-driven trends in deep learning and on the other hand too attached to what humans think of as intelligence, which often reflects loose thinking about human cognitive capacities. They promote a different direction, taking inspiration from the complex behaviours and capabilities of biological organisms, and focussing on how they interact with the world. Aslan Miriyev and Mirko Kovač describe this view and the need for a new interdisciplinary approach to enable what they call physical artificial intelligence (PAI) in a Comment in this issue.

There are many possible definitions of AI. The authors of the 1955 Proposal for the Dartmouth Summer Research Project on Artificial Intelligence 1 tried to “make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” These complex and mainly human forms of intelligence were initially tackled by methods based on the manipulation of symbols, sometimes referred to as ‘good old-fashioned artificial intelligence (GOFAI)’. In contrast, neuroscience-inspired approaches such as connectionism and neural networks focused on learning and representations, which led to the data-driven deep learning methods that are prominent today.

Indeed, many people now consider AI to be synonymous with deep learning, given the impact of deep learning on applications such as image recognition, language processing and speech recognition, and in its utility for recognizing patterns in data from science and industry. However, deep learning in its current state has limitations: it tends to be data hungry, compute heavy, sensory-based, prone to unexpected mistakes as shown by adversarial examples, and inefficacious for much of cognition and behaviour. Indeed, it has turned out to be possible to beat world champions at Go and chess, but much more challenging to learn the basic cognitive and motor skills of a toddler.

An observation that is sometimes referred to as Moravec’s paradox, originating in the 1980s by researchers in AI and robotics, holds that phenomena considered to be high-level intelligence, such as reasoning, occurred late in evolution and require relatively little computation. By contrast, sensorimotor skills and forms of body regulation such as homeostasis, which are generally considered less intelligent or even unintelligent, are highly evolved, often unconscious, and require much greater computational resources. Moravec’s paradox may explain why it is easier to design an AI system to find the best move in a game of chess than to create a dexterous robotic hand that can pick up the pieces and place them on the board.

In the 1980s, the GOFAI approach was not only overtaken by neural networks, it also came under fire from another movement that pointed to the importance of physical grounding. According to this approach, an intelligent system should have its representations grounded in the physical world. Thus, rather than having an internal model of the world, a robot should use its body and sensors to update its control systems and goal-directed behaviours. These views are famously described by Rodney Brooks in his paper, ‘Elephants don’t play chess’ 2 , who pointed out that the world is its own best model and that “the trick is to sense it appropriately and often enough”.

Since then, many advances have been made in robotics to address this challenge. In the past decade, several directions have come together, in bio-inspired design, materials, actuation and sensing, and control, as well as data-driven approaches, which has led Miriyev and Kovač to propose PAI as a new interdisciplinary approach. They define it as “the theory and practice of creating physical systems capable of performing tasks that are typically associated with intelligent organisms.” It is noteworthy that the authors write ‘performing tasks’ and ‘intelligent organisms’ (rather than humans), referring to the multitude of examples from nature of complex problem-solving features and behaviour, such as honey bees that use optic flow and stereovision to approach landing surfaces, and octopuses that demonstrate extraordinary ingenuity in manoeuvring through challenging spaces.

The PAI approach offers the opportunity to incorporate homeostasis, which is seen as an important process for organisms to regulate their behaviour and adapt to different environments. In a Perspective last year, Man and Damasio observed that the field of soft robotics has advanced to a stage where a process resembling homeostasis could be integrated with intelligent machines 3 . This integration of a body, internal regulatory mechanisms and control could lead to a new class of machines that have intrinsic goals.

Given the multidisciplinary nature of PAI, the authors propose that a structure for education and training is required for researchers to develop the necessary skills. In particular, they describe PAI as consisting of five main disciplines: mechanical engineering, computer science, biology, chemistry and materials science. They further discuss changes that need to be made at the institution and community levels, in order to encourage and support PAI research.

We can expect that what is generally considered as intelligence and ‘artificial intelligence’ will remain in flux. By integrating advances from different disciplines, there is an opportunity to create intelligent machines with ever greater complexity.

McCarthy, J., Minsky, M., Rochester, N. & Shannon, C.E. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (Dartmouth College, 1955); http://jmc.stanford.edu/articles/dartmouth.html

Brooks, R. Robot. Auton. Syst. 6 , 3–15 (1990).

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Dartmouth Summer Research Project: The Birth of Artificial Intelligence

Held in the summer of 1956, the dartmouth summer research project on artificial intelligence brought together some of the brightest minds in computing and cognitive science — and is considered to have founded artificial intelligence (ai) as a field..

In the early 1950s, the field of “thinking machines” was given an array of names, from cybernetics to automata theory to complex information processing. Prior to the conference, John McCarthy — a young Assistant Professor of Mathematics at Dartmouth College — had been disappointed by submissions to the Annals of Mathematics Studies journal. He regretted that contributors didn’t focus on the potential for computers to possess intelligence beyond simple behaviors. So, he decided to organize a group to clarify and develop ideas about thinking machines.

“At the time I believed if only we could get everyone who was interested in the subject together to devote time to it and avoid distractions, we could make real progress”. John McCarthy

John approached the Rockefeller Foundation to request funding for a summer seminar at Dartmouth for 10 participants. In 1955, he formally proposed the project, along with friends and colleagues Marvin Minsky (Harvard University), Nathaniel Rochester (IBM Corporation), and Claude Shannon (Bell Telephone Laboratories).

Laying the Foundations of AI

The workshop was based on the conjecture that, “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

Although they came from very different backgrounds, all the attendees believed that the act of thinking is not unique either to humans or even biological beings. Participants came and went, and discussions were wide reaching. The term AI itself was first coined and directions such as symbolic methods were initiated. Many of the participants would later make key contributions to AI, ushering in a new era.

IBM First Computer

Nathaniel Rochester designs the IBM 701, the first computer marketed by IBM.

“Machine Learning” is coined

Attendee Arthur Samuel coins the term “machine learning” and creates the Samuel Checkers-Playing program, one of the world’s first successful self-learning programs.

Marvin Minsky Wins the Turing Award

Marvin Minsky wins the Turing Award for his “central role in creating, shaping, promoting and advancing the field of artificial intelligence.”

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Artificial Intelligence Coined at Dartmouth

The Dartmouth Summer Research Project on Artificial Intelligence was a seminal event for artificial intelligence as a field.

In 1956, a small group of scientists gathered for the Dartmouth Summer Research Project on Artificial Intelligence, which was the birth of this field of research.

To celebrate the anniversary, more than 100 researchers and scholars again met at Dartmouth for AI@50, a conference that not only honored the past and assessed present accomplishments, but also helped seed ideas for future artificial intelligence research.

The initial meeting was organized by John McCarthy, then a mathematics professor at the College. In his proposal, he stated that the conference was “to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Professor of Philosophy James Moor, the director of AI@50, says that the researchers who came to Hanover 50 years ago thought about ways to make machines more cognizant, and they wanted to lay out a framework to better understand human intelligence.

More Dartmouth Milestones

Hitting the Books: Why a Dartmouth professor coined the term 'artificial intelligence'

I f the Wu-Tang produced it in '23 instead of '93, they'd have called it D.R.E.A.M. — because data rules everything around me. Where once our society brokered power based on strength of our arms and purse strings, the modern world is driven by data empowering algorithms to sort, silo and sell us out. These black box oracles of imperious and imperceptible decision-making deign who gets home loans , who gets bail , who finds love and who gets their kids taken from them by the state . 

In their new book, How Data Happened: A History from the Age of Reason to the Age of Algorithms , which builds off their existing curriculum, Columbia University Professors Chris Wiggins and Matthew L Jones examine how data is curated into actionable information and used to shape everything from our political views and social mores to our military responses and economic activities. In the excerpt below, Wiggins and Jones look at the work of mathematician John McCarthy, the junior Dartmouth professor who single-handedly coined the term "artificial intelligence"... as part of his ploy to secure summer research funding.

Excerpted from How Data Happened: A History from the Age of Reason to the Age of Algorithms by Chris Wiggins and Matthew L Jones. Published by WW Norton. Copyright © 2023 by Chris Wiggins and Matthew L Jones. All rights reserved.

Confecting “Artificial Intelligence”

A passionate advocate of symbolic approaches, the mathematician John McCarthy is often credited with inventing the term “artificial intelligence,” including by himself: “I invented the term artificial intelligence,” he explained, “when we were trying to get money for a summer study” to aim at “the long term goal of achieving human level intelligence.” The “summer study” in question was titled “The Dartmouth Summer Research Project on Artificial Intelligence,” and the funding requested was from the Rockefeller Foundation. At the time a junior professor of mathematics at Dartmouth, McCarthy was aided in his pitch to Rockefeller by his former mentor Claude Shannon. As McCarthy describes the term’s positioning, “Shannon thought that artificial intelligence was too flashy a term and might attract unfavorable notice.” However, McCarthy wanted to avoid overlap with the existing field of “automata studies” (including “nerve nets” and Turing machines) and took a stand to declare a new field. “So I decided not to fly any false flags anymore.” The ambition was enormous; the 1955 proposal claimed “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” McCarthy ended up with more brain modelers than axiomatic mathematicians of the sort he wanted at the 1956 meeting, which came to be known as the Dartmouth Workshop. The event saw the coming together of diverse, often contradictory efforts to make digital computers perform tasks considered intelligent, yet as historian of artificial intelligence Jonnie Penn argues, the absence of psychological expertise at the workshop meant that the account of intelligence was “informed primarily by a set of specialists working outside the human sciences.” Each participant saw the roots of their enterprise differently. McCarthy reminisced, “anybody who was there was pretty stubborn about pursuing the ideas that he had before he came, nor was there, as far as I could see, any real exchange of ideas.”

Like Turing’s 1950 paper, the 1955 proposal for a summer workshop in artificial intelligence seems in retrospect incredibly prescient. The seven problems that McCarthy, Shannon, and their collaborators proposed to study became major pillars of computer science and the field of artificial intelligence:

“Automatic Computers” (programming languages)

“How Can a Computer be Programmed to Use a Language” (natural language processing)

“Neuron Nets” (neural nets and deep learning)

“Theory of the Size of a Calculation” (computational complexity)

“Self-​improvement” (machine learning)

“Abstractions” (feature engineering)

“Randomness and Creativity” (Monte Carlo methods including stochastic learning).

The term “artificial intelligence,” in 1955, was an aspiration rather than a commitment to one method. AI, in this broad sense, involved both discovering what comprises human intelligence by attempting to create machine intelligence as well as a less philosophically fraught effort simply to get computers to perform difficult activities a human might attempt.

Only a few of these aspirations fueled the efforts that, in current usage, became synonymous with artificial intelligence: the idea that machines can learn from data. Among computer scientists, learning from data would be de-​emphasized for generations.

Most of the first half century of artificial intelligence focused on combining logic with knowledge hard-​coded into machines. Data collected from everyday activities was hardly the focus; it paled in prestige next to logic. In the last five years or so, artificial intelligence and machine learning have begun to be used synonymously; it’s a powerful thought-​exercise to remember that it didn’t have to be this way. For the first several decades in the life of artificial intelligence, learning from data seemed to be the wrong approach, a nonscientific approach, used by those who weren’t willing “to just program” the knowledge into the computer. Before data reigned, rules did.

For all their enthusiasm, most participants at the Dartmouth workshop brought few concrete results with them. One group was different. A team from the RAND Corporation, led by Herbert Simon, had brought the goods, in the form of an automated theorem prover. This algorithm could produce proofs of basic arithmetical and logical theorems. But math was just a test case for them. As historian Hunter Heyck has stressed, that group started less from computing or mathematics than from the study of how to understand large bureaucratic organizations and the psychology of the people solving problems within them. For Simon and Newell, human brains and computers were problem solvers of the same genus.

Our position is that the appropriate way to describe a piece of problem-​solving behavior is in terms of a program: a specification of what the organism will do under varying environmental circumstances in terms of certain elementary information processes it is capable of performing... ​Digital computers come into the picture only because they can, by appropriate programming, be induced to execute the same sequences of information processes that humans execute when they are solving problems. Hence, as we shall see, these programs describe both human and machine problem solving at the level of information processes.

Though they provided many of the first major successes in early artificial intelligence, Simon and Newell focused on a practical investigation of the organization of humans. They were interested in human problem-​solving that mixed what Jonnie Penn calls a “composite of early twentieth century British symbolic logic and the American administrative logic of a hyper-​rationalized organization.” Before adopting the moniker of AI, they positioned their work as the study of “information processing systems” comprising humans and machines alike, that drew on the best understanding of human reasoning of the time.

Simon and his collaborators were deeply involved in debates about the nature of human beings as reasoning animals. Simon later received the Nobel Prize in Economics for his work on the limitations of human rationality. He was concerned, alongside a bevy of postwar intellectuals, with rebutting the notion that human psychology should be understood as animal-​like reaction to positive and negative stimuli. Like others, he rejected a behaviorist vision of the human as driven by reflexes, almost automatically, and that learning primarily concerned the accumulation of facts acquired through such experience. Great human capacities, like speaking a natural language or doing advanced mathematics, never could emerge only from experience—​they required far more. To focus only on data was to misunderstand human spontaneity and intelligence. This generation of intellectuals, central to the development of cognitive science, stressed abstraction and creativity over the analysis of data, sensory or otherwise. Historian Jamie Cohen-​Cole explains, “Learning was not so much a process of acquiring facts about the world as of developing a skill or acquiring proficiency with a conceptual tool that could then be deployed creatively.” This emphasis on the conceptual was central to Simon and Newell’s Logic Theorist program, which didn’t just grind through logical processes, but deployed human-​like “heuristics” to accelerate the search for the means to achieve ends. Scholars such as George Pólya investigating how mathematicians solved problems had stressed the creativity involved in using heuristics to solve math problems. So mathematics wasn’t drudgery — ​it wasn’t like doing lots and lots of long division or of reducing large amounts of data. It was creative activity — ​and, in the eyes of its makers, a bulwark against totalitarian visions of human beings, whether from the left or the right. (And so, too, was life in a bureaucratic organization — ​it need not be drudgery in this picture — ​it could be a place for creativity. Just don’t tell that to its employees.)

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Editorial article, editorial: artificial intelligence and machine learning in pediatric surgery.

• 1 Department of Surgery, Division of Pediatric Surgery, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
• 2 Department of Neonatology, Beatrix Children’s Hospital, University Medical Center Groningen, University of Groningen, Groningen, Netherlands

Editorial on the Research Topic Artificial intelligence and machine learning in pediatric surgery

1 Introduction

Cutting-edge technologies are leading to a profound transformation of healthcare ( 1 , 2 ). Among the most promising advancements is the integration of artificial intelligence (AI) into the delicate and critical domain of pediatrics ( 3 – 5 ). We find ourselves at the threshold of a new frontier, envisioning how AI might revolutionize every step of the pediatric patient journey. In this Research Topic, we embark on an exploration of how AI may shape the future of pediatrics and pediatric surgery in particular.

2 Artificial intelligence and machine learning in pediatrics

The term “artificial intelligence” was coined by John McCarthy in 1955, defining it as “the science and engineering to make intelligent machines” ( 6 ). Over the years, AI has evolved into a vast field of computer science, levering technologies like machine learning to perform tasks that were once thought to require human intelligence, such as problem-solving, pattern recognition, and decision-making ( 7 ). In pediatrics, where healthcare providers often face intricate tasks demanding advanced human intelligence, AI has emerged as a transformative ally. The most recent technologies brought forward by AI can provide valuable support by analyzing extensive patient data and offering predictive insights which can be incorporated into early warning systems ( 8 ). Moreover, AI holds the potential to assist medical professionals in making precise diagnoses and suggesting personalized treatment recommendations ( 9 – 11 ). Beyond this, AI's capabilities extend into the operating theatre, where it can provide real-time information, robotic assistance, and procedural guidance, further advancing the field of pediatric surgery ( 12 ).

3 AI throughout the pediatric patient journey

This Research Topic delves into AI's potential future role in pediatrics, stretching even to the prenatal phase with Lin et al. exploring AI's potential in the detection of genomic mutations in congenital surgical diseases Lin et al. By navigating through a number of innovative deep learning models that identify and prioritize variations from big genomic data, they showed that AI can help to detect and understand the potential impact of mutations on disease development. This proactive approach enables timely interventions including preventive or corrective measures before or shortly after birth.

As we progress through the patient journey, AI's influence expands into the childbirth process, assisting healthcare professionals in monitoring and optimizing maternal and neonatal care. For instance, AI can play a pivotal role in analyzing progression data to predict the necessity of procedures like caesarean sections ( 13 , 14 ). Post-birth, high-risk neonates undergo a series of diagnostic tests, including imaging scans and laboratory tests. Ongoing research into computer vision algorithms, utilizing convolutional neural networks for the analysis of medical images, holds the promise of more accurate diagnoses ( 15 , 16 ). Future developments may even witness integration of sophisticated multi-modal algorithms, combining diverse data sources for highly precise predictions of specific medical conditions ( 17 ).

Where preventative measures fall short and critical diseases manifest, AI can step in to facilitate informed decision-making. An interesting example lies in the use of Behavioral Artificial Intelligence Technology, showcased in the study by Van Varsseveld et al. illustrating its potential in supporting physicians with end-of-life decision-making for preterm infants with surgical necrotizing enterocolitis.

Within the operating theatre, AI's potential in guiding surgical procedures becomes increasingly evident. While technologies like the use of indocyanine green fluorescence have proven successful in open-surgery Esposito et al. , envisioning an AI-driven iteration might involve the integration of augmented reality (AR) technologies ( 18 , 19 ). These would enable the visualization of crucial landmarks and surgical paths, providing invaluable guidance to surgeons. Another notable example highlighted in this Research Topic involves machine learning algorithms distinguishing between ventral and dorsal roots during selective dorsal rhizotomy using electro-neurophysiological characteristics Jiang et al. Furthermore, robotic-assisted surgery is not untouched by AI's transformative impact, offering surgeons greater precision, facilitating minimally invasive surgeries, and contributing to reduced incisions, pain, and faster recovery times for pediatric patients ( 20 – 22 ). Natural language processing might allow for automatic surgery reporting, streamlining documentation by extracting key information from the surgical procedure and generating detailed reports ( 23 ).

In postoperative care, AI can play a pivotal role by analyzing patient data to predict and prevent complications, optimizing recovery strategies, and personalizing rehabilitation plans. Moreover, it can streamline healthcare processes by optimizing appointment planning and enhancing overall efficiency in healthcare facilities. In essence, AI's integration into pediatrics encompasses a spectrum of technologies and applications, revolutionizing surgical practices and ultimately improving outcomes for pediatric patients and their families.

4 Ethical, legal and societal aspects (ELSA) of applying AI throughout the patient journey

Integration of AI in pediatrics and pediatric surgery brings along a great deal of interconnected ethical, legal, and social concerns, which are of particular relevance when caring for preterm and critically ill newborns and children.

As the article by Till et al. in this Research Topic explained, optimization of the developed algorithms can have a significant impact on its usability Till et al. By performing a thorough pre-processing procedure, they significantly improved the radiological detection of wrist fractures. Hence, before implementation, it is imperative to carry out iterative development and testing procedures to ensure optimal performance and clinical relevance of the model. While doing so, factors like algorithm bias and transparency must be meticulously considered to prevent disproportionate impacts on the vulnerable pediatric populations ( 24 – 26 ). Additionally, robust safeguards are essential to protect patient privacy. Taking a societal perspective, the deployment of AI-tools must transcend socio-economic boundaries, advocating for equal access to these transformative technologies ( 27 ). Simultaneously, a proactive approach to education is essential, empowering individuals with the knowledge necessary to use the tools optimally ( 28 ). The seamless integration of AI in pediatrics also necessitates the cultivation of trust, ensuring its acceptance as valuable aid rather than a potential source of apprehension ( 29 , 30 ). On the legal front, AI prompts a reevaluation of medical liability and responsibility ( 31 – 33 ). Still, by navigating the social and legal dimensions conscientiously, a seamless integration of AI into pediatrics can be achieved, fostering advancements that benefit patients while upholding ethical, social, and legal standards.

5 Conclusion

In this Research Topic, we provide a glimpse of how AI could be possibly integrated into the pediatric patient journey. AI has the potential to be incorporated at any stage of this journey, catering to the specific needs and preferences of healthcare professionals, parents and patients alike. As we navigate the opportunities and challenges in this transformative era, the continuous evolution of AI holds the key to a future where technology becomes an even more indispensable ally in the pursuit of optimal pediatric care.

Author contributions

RV: Writing – original draft, Writing – review & editing. JH: Supervision, Writing – original draft, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article.

This Editorial was supported by the For Wis(h)dom Foundation (Project 9, 02/02/2022, Baarn, The Netherlands).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: artificial intelligence, machine learning, deep learning, pediatric surgery, neonatal and pediatric care, ELSA, surgical innovation

Citation: Verhoeven R and Hulscher JBF (2024) Editorial: Artificial intelligence and machine learning in pediatric surgery. Front. Pediatr. 12:1404600. doi: 10.3389/fped.2024.1404600

Received: 21 March 2024; Accepted: 1 April 2024; Published: 9 April 2024.

Edited and Reviewed by: Simone Frediani , Bambino Gesù Children’s Hospital (IRCCS), Italy

© 2024 Verhoeven and Hulscher. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Rosa Verhoeven [email protected]

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Artificial Intelligence and Machine Learning in Pediatric Surgery

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