Special Collection
AI-Driven Innovations in Early Lung Cancer Management: From Prevention to Surgical Rehabilitation
Science Editor(s)
Collection Scope
Lung cancer remains a leading global health challenge, with early detection and timely intervention being crucial for improving survival rates and enhancing quality of life. As artificial intelligence (AI) continues to evolve, it has the potential to revolutionize the way we approach the prevention, screening, diagnosis, treatment, and rehabilitation of early-stage lung cancer. This special collection, "AI-Driven Innovations in Early Lung Cancer Management: From Prevention to Surgical Rehabilitation," explores the profound impact of AI on the entire continuum of lung cancer care, with a particular emphasis on its integration into surgical techniques and post-surgical rehabilitation.
With contributions from experts in oncology, surgery, and AI, this collection highlights AI’s transformative role in several areas of early lung cancer management. In prevention and screening, AI-powered imaging, predictive algorithms, and biomarker analysis are enhancing early detection accuracy. AI is also playing an increasingly important role in guiding surgical procedures, including the use of robotic-assisted surgeries, AI-powered navigation systems, and predictive analytics to improve surgical precision and patient outcomes. Furthermore, AI is optimizing personalized treatment strategies and refining clinical decision making, enabling multidisciplinary teams to offer more targeted and individualized care.
Beyond surgery, this collection emphasizes the pivotal role of AI in post-operative care and rehabilitation. AI-driven monitoring tools, wearable devices, and rehabilitation algorithms facilitate early recovery, reduce complications, and support personalized rehabilitation plans. By combining technological advances with patient-centered care, AI enhances not only the clinical decision-making process but also the overall patient experience.
This collection aims to explore the synergy between cutting-edge AI technology and compassionate care, showcasing how AI is empowering both patients and healthcare providers to make informed, precise, and humanistic decisions throughout the entire lung cancer treatment journey.
With contributions from experts in oncology, surgery, and AI, this collection highlights AI’s transformative role in several areas of early lung cancer management. In prevention and screening, AI-powered imaging, predictive algorithms, and biomarker analysis are enhancing early detection accuracy. AI is also playing an increasingly important role in guiding surgical procedures, including the use of robotic-assisted surgeries, AI-powered navigation systems, and predictive analytics to improve surgical precision and patient outcomes. Furthermore, AI is optimizing personalized treatment strategies and refining clinical decision making, enabling multidisciplinary teams to offer more targeted and individualized care.
Beyond surgery, this collection emphasizes the pivotal role of AI in post-operative care and rehabilitation. AI-driven monitoring tools, wearable devices, and rehabilitation algorithms facilitate early recovery, reduce complications, and support personalized rehabilitation plans. By combining technological advances with patient-centered care, AI enhances not only the clinical decision-making process but also the overall patient experience.
This collection aims to explore the synergy between cutting-edge AI technology and compassionate care, showcasing how AI is empowering both patients and healthcare providers to make informed, precise, and humanistic decisions throughout the entire lung cancer treatment journey.
Keywords
Lung cancer, early detection, AI-assisted surgery, robotic surgery, precision oncology, personalized treatment, surgical navigation, AI in screening, biomarker discovery, post-surgical rehabilitation, AI in healthcare, multidisciplinary care, patient-centered approach
Deadline for Submission: Ongoing
Ongoing
Submission Information
For Author Instructions, please refer to https://www.oaepublish.com/ais/author_instructions
For Online Submission, please login at https://oaemesas.com/login?JournalId=ais&SpecialCollectionId=241129
Contacts: Zoey Han, Managing Editor, editorialoffice@aisjournal.net
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