Hybrid Intelligence Springer

Bonisiwe Shabane

-Nov 29, 2025, 9:13 PM

This chapter offers a comprehensive overview of hybrid intelligence, through which humans collaborate with artificial intelligence (AI) systems to enhance human and AI capabilities while ensuring that human values, needs, and authority remain central. In line with the principles of Human-Centered AI (HCAI), hybrid intelligence leverages the complementary strengths of humans and AI to create systems that augment, rather than replace, human decision-making and creativity. The chapter discusses how hybrid intelligence prioritizes human oversight, controllability, authority, and ethical considerations, ensuring that AI serves to enhance human well-being and aligns with societal values. It also addresses recent technological advancements, including foundation models, which have highlighted the importance of hybrid intelligence in fields such as healthcare, decision support, and innovation. Alongside these developments, the chapter emphasizes critical ethical and social challenges, such as fairness, accountability, trust, and privacy, within an HCAI framework. The chapter concludes by highlighting future research directions that integrate technical, social, and ethical perspectives to create sustainable, human-centered hybrid intelligence systems that prioritize human agency oversight as well as ethical design.

This is a preview of subscription content, log in via an institution to check access. Abhivardhan. (2025). Data Governance. In W. Xu (Ed.), Handbook of Human-Centered Artificial Intelligence (pp.

1–61). Springer. Allen, R. T., & Choudhury, P. (2022). Algorithm-augmented work and domain experience: The countervailing forces of ability and aversion.

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Scientific Reports volume 15, Article number: 41200 (2025) Cite this article Medical image segmentation is vital for precise identification and analysis of anatomical structures and pathological regions, yet traditional models often fall short in aligning with clinical workflows, requiring extensive manual correction even when overall... To address this gap, we introduce HybridMS, a hybrid intelligence framework designed to maintain high segmentation accuracy while substantially reducing clinician workload through selective human intervention. HybridMS employs an uncertainty-driven feedback mechanism that selectively triggers clinician input only for cases predicted to be challenging, thereby avoiding unnecessary manual review. Corrected cases are prioritised during retraining through a weighted update strategy, enabling the model to adapt more effectively to clinically relevant errors. This design minimises intervention frequency while preserving segmentation quality.

Evaluated on lung segmentation in chest X-rays for tuberculosis detection, HybridMS achieved comparable or improved performance over the baseline MedSAM model (Dice: 0.9538 vs. 0.9435; IoU: 0.9126 vs. 0.8941) with consistent boundary quality in difficult cases. For the subset of cases identified as challenging (baseline Dice < 0.92), HybridMS reduced mean Hausdorff Distance and Average Symmetric Surface Distance, demonstrating more stable anatomical boundaries. Workflow efficiency was markedly improved: in a preliminary timing study with radiologists, average annotation time was reduced by approximately 82% for standard cases and 60% for challenging cases, without compromising accuracy. By combining targeted human oversight with automated refinement, HybridMS demonstrates that stable segmentation performance can be achieved with significantly lower annotation effort, offering a clinically viable pathway for efficient and reliable deployment in diagnostic...

Medical image segmentation is a cornerstone in clinical practice, enabling the precise identification and analysis of anatomical structures and pathological regions. Accurate segmentation is essential for various medical tasks, including diagnosis, treatment planning, and monitoring of disease progression. By dividing images into meaningful segments, clinicians can better identify critical regions such as tumors, organs, and other anatomical structures. This segmentation process is crucial for ensuring accurate diagnoses and effective treatment plans, which can significantly impact patient outcomes1. Traditionally, medical image segmentation has relied on manual annotation by experts2, which, while accurate, is both time-consuming and labor-intensive. Moreover, manual segmentation is subject to significant variability between observers, leading to inconsistencies in the delineation of structures, particularly in complex cases3.

These challenges have prompted the development of automated segmentation methods, aiming to reduce the burden on clinicians and improve the consistency and efficiency of the segmentation process. One particularly challenging area in medical imaging is the segmentation of lung X-rays for the detection and analysis of tuberculosis (TB). Tuberculosis remains a major global health burden and a leading cause of death from infectious diseases globally, making early detection and precise localization of affected regions critical for successful treatment. Chest X-rays are commonly used as the initial imaging modality due to their widespread availability and cost-effectiveness4. However, the complex and variable anatomy of the chest, coupled with overlapping structures, makes accurate segmentation of TB in X-rays particularly difficult. School of Computer Science and Applied Mathematics (CSAM), University of the Witwatersrand, Johannesburg, South Africa

Faculty of Technology, Environmental and Social Sciences, Western Norway University of Applied sciences, Haugesund, Norway Instrumentation and Control Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India Mathematics and Computer Science, Coal City University, Enugu, Nigeria Symbiosis Institute of Technology, Symbiosis International University, Pune, India

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Ajit Kumar Verma is a Professor (Technical Safety), Western Norway University of Applied Sciences, Haugesund, and has been in Norway since 2012. He was a Professor /Senior (HAG) scale Professor for around 15 years at IIT Bombay with the Reliability Engineering/Department of Electrical Engg. He was an adjunct at the University of Stavanger, Norway and has been a Guest Professor at the Luleå University of Technology for the past several years. He was awarded (2017) Honorary Professor at Amity University, India and also a Life Time Achievement awards at IIT Madras by SRESA and also recently at Kurukshetra University in 2024. He has supervised/co-supervised 40 PhDs and over 100 Masters theses( IIT Bombay, India; HVL, Norway; Univ. of Gåvle, Sweden; LJM Univ, UK; WMG, Warwick Univ, UK) .

He has jointly edited a dozen books published by Springer and has jointly authored 8 books. He has over 250 publications in various journals and conferences. Springer has published a book in 2020 to honour him for his research contributions. He has served as a Guest Editor of over a dozen Special Issues of various journals including IEEE Transactions besides being the patron and founding editor/editor in chief in various journals. He has been a Patron/General Chair/ Conference Chair of various International Conferences. Dr.

Om Prakash Verma is presently associated with Dr. B R Ambedkar National Institute of Technology Jalandhar, Punjab, India since January 2018 as an Assistant Professor in the Department of Instrumentation and Control Engineering. He has almost 11 years of teaching experience. He did his Ph.D. from IIT Roorkee, M. Tech.

from Dr. B R Ambedkar NIT Jalandhar and B.E. from Dr. B R Ambedkar University Agra. He is presently working on ISRO Sponsored Project as a PI. He has published more than 30 research papers in SCI/Scopus/ESI indexed Journals.

He has recently published a paper in Renewable & Sustainable Energy Reviews, (IF: 12.110). He has guided 4 M. Tech. students and supervising 6 PhD Students. Dr. Michael Onyema Edeh is currently the Head of Department, Mathematics and Computer Science at Coal City University, Nigeria.

He is also an Adjunct Professor at Shobhit University, India. He was listed among the Top Scientists in Nigeria by Elsevier (SciVal) 2021-2024. Michael is a Recipient of the prestigious Chancellor's Award for Best Staff of the Year 2020, and Vice Chancellor’s Award for research Excellence 2023 both at Coal City University. He is also the current Chairman of Nigeria Computer Society (NCS), Enugu State Chapter, Nigeria. He is also an Adjunct Faculty, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India. Dr.

Michael has published over 100 scholarly papers in reputable journals, and also acted as an editor and Reviewer for many top journals. He has interest in Cybersecurity, Education, Machine Learning and cloud computing. Dr. Jitendra Rajpurohit, a PhD in Nature Inspired Computing has over 15 years of experience in academics and research. He has been associated with many reputed institutions in the past. Currently, he is serving as an Associate Professor at Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune.

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