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From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems

arXiv:2503.01424v4 Announce Type: replace Abstract: Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents a systematic review of the progress in this domain. Specifically, we organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. Hypothesis formulation involves knowledge synthesis and hypothesis generation. Hypothesis validation includes the verification of scientific claims, theorem proving, and experiment validation. Manuscript publication encompasses manuscript writing and the peer review process. Furthermore, we identify and discuss the current challenges faced in these areas, as well as potential future directions for research. Finally, we also offer a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process. We hope this paper serves as an introduction for beginners and fosters future research. Resources have been made publicly available at https://github.com/zkzhou126/AI-for-Research.
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An eyecare foundation model for clinical assistance: a randomized controlled trial

Nature Medicine, Published online: 28 August 2025; doi:10.1038/s41591-025-03900-7

Trained and validated on multimodal data from 14.5 million images from multicountry datasets, a foundation model is shown to increase diagnostic and referral accuracy of clinicians when used as an assistant in a trial involving 16 ophthalmologists and 668 patients.
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Single-Gene Mutations in Hepatocellular Carcinoma: Applications and Challenges in Precision Medicine

Int J Med Sci. 2025 Jul 10;22(13):3268-3276. doi: 10.7150/ijms.117603. eCollection 2025.

ABSTRACT

Hepatocellular carcinoma (HCC) is a genetically heterogeneous malignancy in which single-gene mutations serve as critical drivers of tumor initiation, progression, and therapeutic resistance. Advances in high-throughput genomics and liquid biopsy technologies have highlighted the clinical utility of mutations in genes such as TP53, CTNNB1, and TERT as diagnostic, prognostic, and predictive biomarkers. These mutations disrupt key oncogenic pathways, modulate the tumor immune microenvironment, and contribute to intratumoral heterogeneity, complicating disease management. Mutation-guided precision medicine, including telomerase inhibitors, Wnt/β-catenin pathway modulators, and immune checkpoint blockade, offers promising avenues for individualized treatment in HCC. However, challenges persist in translating these findings into clinical practice due to mutation complexity, resistance mechanisms, and limitations in biomarker standardization. Emerging strategies such as multi-omics integration, artificial intelligence, and gene editing technologies hold potential to overcome these barriers and facilitate the development of personalized therapeutic regimens. This review summarizes the molecular mechanisms, clinical applications, and translational challenges of single-gene mutations in HCC, with the aim of guiding future research and precision oncology.

PMID:40765562 | PMC:PMC12320797 | DOI:10.7150/ijms.117603

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Application of artificial intelligence and machine learning in lung transplantation: a comprehensive review

Front Digit Health. 2025 May 1;7:1583490. doi: 10.3389/fdgth.2025.1583490. eCollection 2025.

ABSTRACT

Lung transplantation (LTx) is an effective method for treating end-stage lung disease. The management of lung transplant recipients is a complex, multi-stage process that involves preoperative, intraoperative, and postoperative phases, integrating multidimensional data such as demographics, clinical data, pathology, imaging, and omics. Artificial intelligence (AI) and machine learning (ML) excel in handling such complex data and contribute to preoperative assessment and postoperative management of LTx, including the optimization of organ allocation, assessment of donor suitability, prediction of patient and graft survival, evaluation of quality of life, and early identification of complications, thereby enhancing the personalization of clinical decision-making. However, these technologies face numerous challenges in real-world clinical applications, such as the quality and reliability of datasets, model interpretability, physicians' trust in the technology, and legal and ethical issues. These problems require further research and resolution so that AI and ML can more effectively enhance the success rate of LTx and improve patients' quality of life.

PMID:40376618 | PMC:PMC12078212 | DOI:10.3389/fdgth.2025.1583490

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Epigenetic regulation during cancer transitions across 11 tumour types

Nature, Published online: 01 November 2023; doi:10.1038/s41586-023-06682-5

A pan-cancer epigenetic and transcriptomic atlas identifies epigenetic drivers associated with cancer transitions.
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WNT2–SOX4 positive feedback loop promotes chemoresistance and tumorigenesis by inducing stem-cell like properties in gastric cancer

Oncogene, Published online: 26 August 2023; doi:10.1038/s41388-023-02816-1

WNT2–SOX4 positive feedback loop promotes chemoresistance and tumorigenesis by inducing stem-cell like properties in gastric cancer
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Histone demethylase KDM5D upregulation drives sex differences in colon cancer

Nature, Published online: 21 June 2023; doi:10.1038/s41586-023-06254-7

A murine colorectal cancer (CRC) model shows that mutant KRAS-STAT4-mediated upregulation of Y chromosome KDM5D contributes to the sex differences in KRAS-mutant CRC, providing an actionable therapeutic strategy for metastasis risk reduction for men afflicted with KRAS-mutant CRC.
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