Artificial Intelligence in Medicine

Aims & Scope

Artificial Intelligence in Medicine publishes original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI) in medicine, medically-oriented human biology, and health care.

Artificial intelligence in medicine may be characterized as the scientific discipline pertaining to research studies, projects, and applications that aim at supporting decision-based medical tasks through knowledge- and/or data-intensive computer-based solutions that ultimately support and improve the performance of a human care provider.

Artificial Intelligence in Medicine considers for publication manuscripts that have both: • Potential high impact in some medical or healthcare domain; • Strong novelty of method and theory related to AI and computer science techniques.

Artificial Intelligence in Medicine papers must refer to real-world medical domains, considered and discussed at the proper depth, from both the technical and the medical points of view.

The inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly recommended.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	6.2
2024	6.10

SJR (SCImago Journal Rank)

Year	Value
2024	1.396

Quartile

Year	Value
2024	Q1

Journal Rank

Year	Value
2024	2714

Journal Citation Indicator

Year	Value
2024	3848

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science
• Medline
• Embase

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Medicine, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Machine learning for medical diagnosis: history, state of the art and perspective

  Citation: 1189

  Authors: Igor

• Predicting breast cancer survivability: a comparison of three data mining methods

  Citation: 823

  Authors: Dursun, Glenn, Amit

• Smart wearable systems: Current status and future challenges

  Citation: 784

  Authors: Marie, Daniel, Jean-Yves, Christophe, Eric

• Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

  Citation: 640

  Authors: Jonathan, Charlotta, Renato

• The coming of age of artificial intelligence in medicine

  Citation: 458

  Authors: Vimla L., Edward H., Mario, Peter, Michael R., Riccardo, Ameen

• Missing data imputation using statistical and machine learning methods in a real breast cancer problem

  Citation: 453

  Authors: José M., Ignacio, Pedro J., Emilio, Nuria, Miguel, Leonardo

• Uniqueness of medical data mining

  Citation: 411

  Authors: Krzysztof J., G.

• Local binary patterns variants as texture descriptors for medical image analysis

  Citation: 408

  Authors: Loris, Alessandra, Sheryl

• A novel kernelized fuzzy C-means algorithm with application in medical image segmentation

  Citation: 395

  Authors: Dao-Qiang, Song-Can

• GANs for medical image analysis

  Citation: 353

  Authors: Salome, Christoph, Arjan, Bram, Nassir, Shadi, Anirban

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