| | | | | | |

Education in the era of Artificial Intelligence

ByMaurício V. Brant Pinheiro

We teachers are certainly oppressing our students when we expect them to show up for class, or put them through repeated drafts of their papers, (…) But we’re also liberating our students, (…) by equipping them with instruments of legibility, and by setting them ashore — as we ultimately must — on some uncharted continent of the mind which it will be up to them to explore. – John Lewis Gaddis – The Landscape of History: How Historians Map the Past.

Mama Kim knew from studying the vast clinical data on children with Asperger’s that they developed different patterns of thinking and cognitive functioning. Many of these distinctive attributes would remain with them all their lives. Such children typically benefited from highly individualized education methods. The way Mama Kim saw it, children with Asperger’s syndrome didn’t need to be normal. Like any child, they needed only to become their best selves. – Kai-Fu Lee and Chen Qiufan – AI 2041: Ten Visions for our Future.

19–29 minutes

Prof. Maurício V. B. Pinheiro and Prof. Sérgio V. B. Pinheiro
Universidade Federal de Minas Gerais

1. Introduction

The integration of artificial intelligence (AI) into educational systems holds great potential for transformation. As AI technologies continue to advance and become more prevalent in classrooms, they have the potential to fundamentally change the way teachers impart knowledge and students acquire understanding. AI, known for its ability to emulate cognitive functions typically associated with humans, such as learning and problem-solving, offers promising applications for enhancing education, particularly in fields such as healthcare, STEM, humanities, and language studies, which rely on rapidly expanding bodies of information.

The 2018 report “The Future of Jobs” by the World Economic Forum highlighted several ways AI could positively impact learning. AI systems capable of personalizing instruction based on individual needs and learning styles have the potential to engage students in new and more effective ways. Automation of routine tasks, such as grading assignments, could free up educators’ time for more meaningful interactions with students. Additionally, AI shows potential for providing detailed and timely feedback that helps students improve their skills and knowledge.

However, integrating advanced technologies like AI into education also brings about complex issues. Questions arise regarding the development of algorithms and the potential for unintended bias in educational AI systems. Furthermore, as certain educational tasks become automated through AI tools, the nature and number of human roles within education may change, raising concerns about job displacement. This paper aims to explore both the opportunities and challenges presented by AI’s growing role in classrooms. By examining its dynamic relationship with teaching and learning, a deeper understanding of AI’s impact on education may emerge.

2. AI in Medical Education – Showcasing Concepts

Artificial Intelligence (AI) presents a range of advantages for both healthcare students and instructors. What’s more, the innovations in AI within the medical education sector will serve as a showcase of concepts and new educational paradigms that can be readily extended to diverse fields of knowledge. Lets have a look at each one of these capabilities.

2.1. Personalized Learning and Predictive Analysis:

AI in medical education offers several key advantages, including personalized learning experiences tailored to individual students’ needs and strengths. AI algorithms assess students’ strengths and weaknesses, offering customized learning materials to facilitate self-paced learning and targeted improvement in areas of challenge. This personalized approach enhances students’ potential and promotes a more efficient grasp of intricate medical concepts.

Additionally, AI empowers adaptive assessments, which dynamically adjust based on students’ performance in real-time. This feature provides instant feedback, enabling students to focus their efforts on improving their comprehension of complex medical concepts. Furthermore, adaptive assessments identify areas requiring additional support, allowing instructors to intervene early and deliver personalized assistance to struggling students.

AI algorithms delve into student data to identify patterns and predict academic performance, a facet known as predictive analytics. Instructors leverage this data to provide extra support to students grappling with specific subjects. Predictive analytics aids educators in recognizing potential learning gaps before they escalate, ensuring timely assistance to keep students on track.

Furthermore, AI-driven data analysis enables instructors to make informed, data-driven decisions to enhance student outcomes. Early intervention ranks among the primary benefits, with AI algorithms issuing early warning signals about students at risk of falling behind. This timely intervention minimizes the risk of academic failure.

Improved student outcomes represent another advantage, with AI algorithms deciphering performance data to foresee academic results. Instructors can adjust their teaching methods or offer extra support based on this information, ultimately enhancing academic performance and overall student outcomes.

2.2. Improved simulations and virtual reality experiences

AI has the potential to greatly enhance medical education by providing improved simulations and virtual reality experiences that provide students with hands-on experience in diagnosing and treating patients. These experiences can be used to complement and enhance traditional teaching methods, leading to improved student outcomes and more efficient use of resources. Some of the benefits of AI in this area include:

  • Realistic simulations: AI algorithms can create realistic medical simulations that provide students with hands-on experience in diagnosing and treating patients. This can be particularly useful in training students on complex surgical procedures and emergency medicine scenarios.
  • Immersive virtual reality experiences: AI-powered virtual reality experiences can immerse students in realistic medical environments and allow them to practice procedures and treatments in a safe and controlled setting. This can provide students with valuable experience before they are faced with real-life patients.
  • Increased efficiency: AI simulations can be used to quickly and efficiently evaluate student performance and provide feedback in real-time. This, again, can help instructors save time and reduce the need for manual grading and assessment.
  • Accessibility: AI simulations and virtual reality experiences can be easily accessed by students from any location, at any time. This can increase access to quality medical education and reduce barriers to learning.
  • Cost-effectiveness: AI simulations and virtual reality experiences can provide students with realistic online hands-on experience at a lower cost than traditional methods, such as cadaver labs or live animal surgeries.

2.3. Automated grading and feedback

The use of AI can significantly improve the grading and feedback process by providing automated and efficient solutions in medical education. AI offers several benefits, including real-time feedback, increased accuracy, time savings, customized feedback, and data-driven insights.

AI algorithms can quickly grade students’ assignments and provide real-time feedback, reducing the wait time for students to receive feedback on their work. This can help students improve their understanding of complex medical concepts and target their learning efforts to areas where they need the most improvement. Moreover, AI can grade assignments and provide feedback in a consistent and objective manner, reducing the potential for human error and bias. This can improve the overall fairness and accuracy of the grading process.

By using AI for grading, instructors can save time, freeing them up for other important tasks, such as preparing lectures, meeting with students, and conducting research.

2.4. Virtual tutors

AI has also the potential to revolutionize medical education by creating virtual tutors that provide students with a personalized learning experience. These virtual tutors can offer several benefits to students, including personalized support, increased accessibility, and increased efficiency.

Personalized support is one of the primary benefits of AI-powered virtual tutors in medical education. Virtual tutors can analyze individual students’ strengths, weaknesses, and learning styles to provide them with customized support and guidance. By focusing on areas where students need the most improvement, virtual tutors can help students enhance their academic performance and achieve better outcomes.

Another benefit of AI virtual tutors is increased accessibility. With an internet connection, students can access virtual tutors from anywhere, making it easier for students who live in remote areas or who need to balance their studies with other responsibilities. This accessibility allows students to receive help and support on their own schedule, leading to a more convenient and accessible learning experience.

Lastly, AI virtual tutors can provide support and guidance 24/7 without the need for human supervision, increasing efficiency. Instructors can use virtual tutors to manage their workload more efficiently and provide students with timely and consistent support.

In conclusion, the integration of AI in medical education holds the potential to significantly enhance learning outcomes and elevate the quality of medical training. AI has the capacity to provide personalized learning experiences, offering customized educational materials, adaptive assessments, virtual tutoring, and predictive analytics. Furthermore, AI-driven simulations and virtual reality experiences can offer realistic and immersive medical training, streamlining the grading and feedback process. Notably, these concepts are not confined to medical education alone, as many of these innovations can be adapted to other areas of education, such as STEM, humanities, and more. By harnessing the advantages of AI, education, including medical training, can become more accessible, efficient, and effective in providing high-quality instruction to future professionals.

3. AI in STEM Education

Artificial Intelligence (AI) is rapidly reshaping the landscape of STEM (Science, Technology, Engineering, and Mathematics) education, ushering in a new era of enriched learning and heightened engagement in these critical subjects. Considering that many of the capabilities of AI mentioned above for medical education can also be adapted for STEM education, we take a step further by offering concrete examples of AI applications in Science, Technology, Engineering, and Mathematics education. This sheds light on AI’s potential to elevate educational experiences and equip students with the skills and knowledge crucial for success in these fields.

In Science Education, AI paves the way for groundbreaking opportunities for students to delve into complex scientific concepts. Interactive AI simulations allow students to experiment across various scientific domains, enabling them to observe processes, manipulate variables, and gain a deeper understanding of intricate scientific principles. Moreover, AI-driven data analysis and visualization tools simplify the interpretation of scientific data, providing students with the ability to work with extensive datasets and extract invaluable insights from their experiments.

The realm of Technology Education is experiencing a revolution thanks to AI’s central role in teaching coding and programming. AI-supported platforms provide interactive coding lessons, coding challenges, and real-time coding feedback, offering students hands-on experience and immediate guidance. Educational tools present coding concepts in a playful and interactive manner, ensuring that technology education becomes an enjoyable and accessible experience. Furthermore, AI exposes students to the practical application of programming skills, empowering them to create real-world applications with ease.

In Mathematics Education, AI enhances problem-solving capabilities by offering step-by-step solutions and real-time feedback on mathematical equations. Customized math learning is another domain where AI excels, personalizing lessons to align with each student’s unique needs and strengths, ensuring a profound understanding of mathematical concepts.

The integration of AI into STEM education opens up a multitude of opportunities to transform the learning experience. By providing personalized, interactive, and practical learning environments, AI equips students with the knowledge and skills indispensable for success in these pivotal fields.

Now, let’s delve into these transformative prospects within the realms of Science, Technology, Engineering, and Mathematics education, providing more insights and concrete examples for each discipline (including top resources). Some examples are:

  • Interactive AI Simulations: AI simulations make complex scientific phenomena accessible and engaging. For instance, PhET Interactive Simulations offers a wide range of interactive simulations (Physics, Chemistry, Math, Earth Sciences and Biology) that enable students to experiment with virtual laboratories. These simulations allow students to observe scientific processes, manipulate variables, and develop a deeper understanding of scientific concepts.
  • Data Analysis and Visualization: AI-driven tools aid students in analyzing and visualizing scientific data. For example, Tableau, a data visualization platform, employs AI to simplify data interpretation. This makes it easier for students to work with complex datasets, draw meaningful conclusions, and gain insights from their experiments.
  • Coding and Programming with AI: AI facilitates the teaching and learning of coding and programming. Codecademy, for instance, is an online platform that employs AI to offer interactive coding lessons, coding challenges, and real-time coding feedback. Students can experiment with programming languages, develop coding skills, and receive immediate feedback.
  • Interactive Coding Lessons: Educational tools such as Scratch and Tynker engage students in interactive coding lessons and activities. These platforms introduce coding concepts in a playful manner, making technology education enjoyable and accessible.
  • Real-World Application of Programming Skills: AI also exposes students to the practical application of programming skills. Platforms like Kaggle, Google Colab, App Inventor allow students to build mobile apps, while web development tools like Wix and Worldpress employ AI to simplify website creation. These hands-on experiences give students the opportunity to apply their programming knowledge in real-world contexts.
  • AI-Enhanced Problem Solving: AI is increasingly used to assist students in solving complex mathematical problems. For instance, Wolfram Alpha, an AI-driven computational engine, provides step-by-step solutions to mathematical equations, offering real-time feedback on problem-solving techniques.
  • Customized Math Learning: Personalization in mathematics education is significantly enhanced by AI. Adaptive learning platforms like DreamBox use AI algorithms to tailor math lessons (and also Reading) to individual students. These platforms analyze a student’s strengths and weaknesses, adjust content difficulty, and offer personalized exercises, fostering a deeper understanding of mathematical concepts.

Incorporating AI into STEM education holds the potential to significantly enhance learning experiences. With its ability to provide personalized, interactive, and practical learning opportunities, AI equips students with the skills and knowledge needed for success in these critical fields.

4. Humanities Education on the Verge

The landscape of humanities education, spanning a diverse spectrum from language acquisition to the pursuit of historical knowledge, is currently in the throes of a profound metamorphosis within the realm of AI, primarily driven by Natural Language Processing and the capabilities of Large Language Models (LLMs), which draw upon extensive datasets. This field is standing at the precipice of a monumental transformation spurred by AI. The anticipated impact of Large Language Models extends beyond educational innovation; it carries the potential to reshape the traditional roles of educators, potentially diminishing their conventional teaching responsibilities.

In sharp contrast to the objective and pioneering nature of STEM and Medicine, fields that tangibly contribute to advancements in the quality of life, humanities faculties have faced scrutiny for their research’s subjective nature and the existence of biases within scholarly endeavors. These biases have, at times, led to a distorted and context-dependent portrayal of knowledge, susceptible to human error and the ebb and flow of political dynamics. Additionally, the prevalence of these biases, particularly in the digital realm, is seen as contributing to the perpetuation of indoctrination and the deepening of societal polarization. Humanities faculties bear a shared responsibility in this regard, as they too influence the evolving political landscape. This stark contrast underscores the potential of AI to offer an objective and comprehensive repository of knowledge, thereby mitigating indoctrination within the humanities. Here are some examples:

  • AI-Enhanced Language Learning: Large Language Models (LLMs) can revolutionize language learning by offering personalized instruction and practice. They provide real-time feedback, adapt content to individual needs, and even utilize synthetic voices to enhance language proficiency. Platforms like Duolingo have already incorporated AI for more effective language education.
  • Language Translation and Conversation Practice: AI-driven language translation tools and conversational practice applications, like Babbel or Rosetta Stone, leverage Large Language Models to provide accurate translations and immersive language exchange experiences. This aids learners in developing practical language skills.
  • AI in History and Social Sciences: Large Language Models are invaluable for historical and social sciences research. They serve as powerful research assistants, sifting through vast volumes of historical data to extract patterns, insights, and trends. By doing so, they facilitate unbiased and data-driven scholarship.
  • AI-Powered Research Tools: AI-driven tools assist researchers in the humanities by automating tasks like literature reviews, summarizing research papers, and even generating research questions. These tools can streamline the research process and help scholars discover new perspectives.
  • AI-Generated Art and Music: AI is making strides in the creative arts by generating original pieces of art and music. Artists and musicians can collaborate with AI to inspire new and innovative creations, pushing the boundaries of human creativity.
  • Creative Writing Assistance: Large Language Models excel in assisting writers with creative content generation. They can provide prompts, generate ideas, and even aid in the development of stories, essays, and poetry. This collaborative approach enhances the creative process for writers of all levels.

As AI continues to evolve, it is poised to revolutionize humanities education by offering objective, encyclopedic knowledge and eliminating indoctrination. Large Language Models, in particular, hold significant promise in this transformation. They provide opportunities for personalized learning, unbiased historical research, and creative collaborations across various humanities disciplines. This shift has the potential to not only enhance the quality of education but also empower students and researchers in the humanities to explore new horizons and foster a more balanced and comprehensive understanding of the world.

5. Open Educational Resources (OER)

The convergence of Open Educational Resources (OER) and Artificial Intelligence (AI) represents a significant shift in education. OER, with its capacity to provide high-quality educational materials at minimal cost, has already begun reshaping how people access knowledge. Meanwhile, AI, a transformative technology across various industries, is poised to revolutionize education. When these two forces merge, a realm of opportunities emerges, especially in self-paced learning, creating a harmonious synergy with the potential to redefine education.

One remarkable aspect of this convergence is the ability to provide personalized self-learning experiences. AI goes beyond analyzing learners’ progress; it tailors learning content to suit individual needs and preferences. Platforms like Khan Academy utilize AI to monitor students’ performance and adjust content accordingly. If a student excels in a particular area, AI can propose more challenging exercises or offer additional resources. Conversely, for those encountering difficulty, AI provides supplementary materials, ensuring a deeper understanding and enhancing the learning experience.

Additionally, AI enables adaptive learning. In language learning applications such as Duolingo, mentioned above, AI observes user performance and adapts the difficulty of subsequent lessons. When a user struggles with a specific concept, AI intervenes with extra practice to reinforce understanding and prevent frustration.

Continuous feedback is a cornerstone of AI-powered self-paced learning. In platforms like Coursera or edX, AI evaluates assignments and quizzes, providing instant feedback to learners. This feedback helps students identify their mistakes, understand areas requiring improvement, and develop strategies to enhance their performance. The immediacy of this feedback loop keeps learners engaged and motivated as they actively address their weaknesses and monitor their progress.

Moreover, AI promotes accessibility and inclusivity in education. It can convert text-based OER into audio formats using text-to-speech technology, making educational materials accessible to those with visual impairments. AI can also generate translations and offer sign language support, extending the reach of OER to diverse communities, thereby advancing the democratization of education.

Rather than replacing teachers, AI in education empowers them. By automating administrative tasks like grading and attendance tracking, AI allows educators to focus on providing guidance, mentorship, and addressing individual learning challenges. This results in a more efficient, productive, and fulfilling teaching experience.

Nonetheless, the integration of OER and AI is not without challenges. Data privacy becomes a significant concern, as the collection and storage of student data are crucial in AI-powered self-paced learning. Maintaining data privacy and security to protect sensitive information is imperative. The digital divide, where not all learners have equal access to technology and the internet, can exacerbate educational disparities. Efforts must be made to bridge this divide, ensuring that AI-powered education remains inclusive. Quality control and ongoing curation of OER materials become increasingly important as AI plays a larger role in educational content delivery. Continuous monitoring and improvement of these materials are necessary to maintain educational standards and relevance.

In conclusion, the fusion of Open Educational Resources and Artificial Intelligence is redefining the educational landscape. AI-powered self-paced learning offers a more accessible, engaging, and equitable future for learners worldwide. By harnessing AI’s capabilities, OER transforms into a dynamic and personalized educational experience, catering to individual needs, fostering deep understanding, and allowing educators to focus on mentorship and teaching. The potential for OER and AI integration is limitless, promising a transformative force that will shape the future of education.

5. Ethic considerations

As with any new technology, there are a number of ethical considerations to take into account when using AI in education. It is important to consider these ethical issues in order to ensure that AI is used in a way that is fair, transparent, and respects the privacy and well-being of students and teachers. Some of the ethical concerns surrounding the use of AI in education include bias in algorithms, potential job displacement, privacy concerns, and the need for transparency.

One ethical concern with AI in education is the potential for biased algorithms. AI algorithms are only as good as the data they are trained on, and if the data is biased, the algorithm may also be biased. This can have serious consequences in education, as it could lead to unfairly disadvantaged students. For example, if an AI algorithm is trained on data that is biased against certain groups (e.g., racial or gender bias), then the algorithm may make biased decisions that negatively impact those groups. In order to mitigate the risk of biased algorithms, it is important to ensure that the data used to train AI algorithms is representative of the population being served.

Another ethical concern with AI in education is the potential for job displacement. As AI can automate certain tasks that are currently performed by human teachers, there is a risk that it could lead to job loss in the education sector. It is important to consider the impact of AI on employment and to ensure that there are appropriate policies in place to support those who may be affected by job displacement.

There are also concerns about privacy when it comes to the use of AI in education. As AI collects and analyzes large amounts of data, there are concerns about the privacy of students and teachers. It is important that AI is used in a way that respects the privacy of individuals and that appropriate safeguards are in place to protect this data.

Transparency is another important ethical consideration when it comes to the use of AI in education. It is important that the use of AI is transparent and that students, teachers, and parents are aware of how it is being used. This includes being transparent about the algorithms being used and the data that is being collected and analyzed.

In addition to these ethical considerations, there are also a number of challenges to using AI in education. One challenge is the need for appropriate infrastructure and support. In order to effectively use AI in education, schools and universities need to have the necessary infrastructure and support in place, including access to technology and trained personnel. Another challenge is the need for teacher training and professional development. As AI becomes more prevalent in education, it is important that teachers are trained on how to use and integrate this technology into their classrooms.

6. Conclusions

As we reflect on the journey of integrating AI into education, it becomes evident that AI’s role in learning environments is not static but an ever-evolving force. The dynamic nature of AI implies that its applications will continue to adapt and expand, shaping the educational landscape in increasingly sophisticated ways.

One of the most transformative aspects of AI in education is its potential to personalize learning experiences. This tailoring of instruction to individual students, a hallmark of AI, is poised to revolutionize traditional teaching methods. The ability to cater to the unique strengths, weaknesses, and learning styles of students can greatly enhance the educational experience.

In addition, AI brings efficiency to the educational process through automated grading and immediate feedback mechanisms. This not only lightens the grading burden for educators but also empowers them to redirect their time and attention towards more interactive and engaging teaching, thus improving the overall quality of education.

Moreover, AI’s data analysis capabilities offer invaluable insights into student performance. By interpreting vast datasets, educators gain the ability to make data-driven decisions, identify areas where students may require additional support, and refine teaching methodologies to optimize the curriculum. This data-driven approach enhances the quality and effectiveness of education.

Nevertheless, the journey of integrating AI into education is not without its ethical considerations. To harness the potential of AI effectively, it is imperative to address issues such as bias in algorithms. AI algorithms are only as impartial as the data they are trained on. Bias within the data could lead to unfair educational outcomes, disadvantaging certain groups. It is, therefore, crucial to ensure that the data used for AI training is representative and devoid of biases.

The automation of certain educational tasks through AI also raises concerns about potential job displacement among educators. To mitigate these concerns, proactive policies and strategies must be devised to support educators, potentially redefining their roles to align with the evolving educational landscape.

Additionally, as AI thrives on extensive data collection, protecting student and teacher privacy becomes paramount. Robust privacy measures, data protection policies, and secure infrastructure must be in place to ensure the confidentiality and security of sensitive information.

In essence, the ongoing evolution of AI in learning environments offers immense potential for the improvement of education. While personalized learning, efficient grading, and data-driven insights promise to enhance the educational experience, we must also remain steadfast in addressing ethical challenges. By maintaining transparency, mitigating job displacement concerns, and ensuring robust privacy protections, we can strike a balance between AI’s potential and the ethical and social dimensions that underpin education’s progress in the digital age.

References:

Xu, Weiqi, and Fan Ouyang. “The application of AI technologies in STEM education: a systematic review from 2011 to 2021.” International Journal of STEM Education 9.1 (2022): 1-20.

AI helps create personalized math lessons for students, John Roach, July 13, 2022 https://news.microsoft.com/source/features/ai/eedi-online-math-quiz/

bin Mohamed, M. Z., Hidayat, R., binti Suhaizi, N. N., bin Mahmud, M. K. H., & binti Baharuddin, S. N. (2022). Artificial intelligence in mathematics education: A systematic literature review. International Electronic Journal of Mathematics Education, 17(3), em0694.

Goksel, Nil, and Aras Bozkurt. “Artificial intelligence in education: Current insights and future perspectives.” Handbook of Research on Learning in the Age of Transhumanism. IGI Global, 2019. 224-236.

World Economic Forum. (2018). The future of jobs report 2018. Geneva, Switzerland: World Economic Forum.

Robertson, M. Artificial intelligence in education. Nature. 1976; 262 (5570): 435-437. doi: 10.1038/262435a0.

Mosadeghi S, Reid MW, Martinez B, Rosen BT, Spiegel BMR. Feasibility of an Immersive Virtual Reality Intervention for Hospitalized Patients: An Observational Cohort Study. JMIR Ment Health. 2016; 3: e28. doi: 10.2196/mental.6409.

Chan KS & Zary N. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Med Educ. 2019; 5 (1): e13930. doi: 10.2196/13930.

Sapci AH & Sapci HA. Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review. JMIR Med Educ. 2020; 6 (1): e19285. doi: 10.2196/19285.

Zhang L, Lu J, Dong Y, et al. Artificial Intelligence in Healthcare: Past, Present and Future. Semin Cancer Biol. 2020; 65: 1-14. doi: 10.1016/j.semcancer.2019.10.016.

Kockro RA, Amaxopoulou C, Killeen T, et al. Augmented Reality in Neurosurgery: A Review of Current Concepts and Emerging Applications. Front Surg. 2020; 7: 567420. doi: 10.3389/fsurg.2020.567420.

Pucchio A, Eisenhauer EA, & Moraes FY. Medical students need artificial intelligence and machine learning training. Nature Biotechnology. 2021; 39: 388-389. doi: 10.1038/s41587-021-00868-w.

Grunhut J, Marques O, Wyatt ATM. Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum. JMIR Med Educ. 2022; 8 (2): e35587. doi: 10.2196/35587.

Gillissen A, Kochanek T, Zupanic M, Ehlers J. Medical Students’ Perceptions towards Digitization and Artificial Intelligence: A Mixed-Methods Study. Healthcare (Basel). 2022; 10: 723. doi: 10.3390/healthcare10040723.

Vijlbrief D, Dudink J, van Solinge W, Benders M & Haitjema S. From computer to bedside, involving neonatologists in artificial intelligence models for neonatal medicine. Pediatric Research. 2022; 91 (1): 85-91. doi: 10.1038/s41390-021-01487-7.

Copyright 2026 AI-Talks.org

Similar Posts