Explainable artificial intelligence for precision medicine in acute myeloid leukemia

Gimeno, Marian; José‐Eneriz, Edurne San; Villar, Sara; Agirre, Xabier; Prósper, Felipe; Rubio, Angel; Carazo, Fernando

doi:10.3389/fimmu.2022.977358

Cited by 19 publications

(26 citation statements)

References 57 publications

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“…48 Therefore, by adding the XAI technology to ML and DL models, the use of AI in healthcare will become more reliable and acceptable. [49][50][51] In addition, before application, the public should be educated on the principles of the medical AI system, including how it works.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in a study involving categorising tuberculosis diagnoses through deep learning chest radiographs, researchers used heat maps to show areas of increased activation of deep learning networks that could be inferred to be important for diagnosis 48. Therefore, by adding the XAI technology to ML and DL models, the use of AI in healthcare will become more reliable and acceptable 49–51. In addition, before application, the public should be educated on the principles of the medical AI system, including how it works.…”

Section: Discussionmentioning

confidence: 99%

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Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

Wu,

Xu,

Bai

et al. 2023

BMJ Open

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ObjectivesMedical artificial intelligence (AI) has been used widely applied in clinical field due to its convenience and innovation. However, several policy and regulatory issues such as credibility, sharing of responsibility and ethics have raised concerns in the use of AI. It is therefore necessary to understand the general public’s views on medical AI. Here, a meta-synthesis was conducted to analyse and summarise the public’s understanding of the application of AI in the healthcare field, to provide recommendations for future use and management of AI in medical practice.DesignThis was a meta-synthesis of qualitative studies.MethodA search was performed on the following databases to identify studies published in English and Chinese: MEDLINE, CINAHL, Web of science, Cochrane library, Embase, PsycINFO, CNKI, Wanfang and VIP. The search was conducted from database inception to 25 December 2021. The meta-aggregation approach of JBI was used to summarise findings from qualitative studies, focusing on the public’s perception of the application of AI in healthcare.ResultsOf the 5128 studies screened, 12 met the inclusion criteria, hence were incorporated into analysis. Three synthesised findings were used as the basis of our conclusions, including advantages of medical AI from the public’s perspective, ethical and legal concerns about medical AI from the public’s perspective, and public suggestions on the application of AI in medical field.ConclusionResults showed that the public acknowledges the unique advantages and convenience of medical AI. Meanwhile, several concerns about the application of medical AI were observed, most of which involve ethical and legal issues. The standard application and reasonable supervision of medical AI is key to ensuring its effective utilisation. Based on the public’s perspective, this analysis provides insights and suggestions for health managers on how to implement and apply medical AI smoothly, while ensuring safety in healthcare practice.PROSPERO registration numberCRD42022315033.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

Wu,

Xu,

Bai

et al. 2023

BMJ Open

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“…It is difficult to use the majority of commercially available cell lines because of the lack of this specific alteration in the majority of them. ME-1 cell line is a unique exception [ 116 ].…”

Section: Aml Cell Lines and Typesmentioning

confidence: 99%

Choosing the Right Cell Line for Acute Myeloid Leukemia (AML) Research

Skopek

Palusińska

Kaczor-Keller

et al. 2023

IJMS

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Immortalized cell lines are widely used in vitro tools in oncology and hematology research. While these cell lines represent artificial systems and may accumulate genetic aberrations with each passage, they are still considered valuable models for pilot, preliminary, and screening studies. Despite their limitations, cell lines are cost-effective and provide repeatable and comparable results. Choosing the appropriate cell line for acute myeloid leukemia (AML) research is crucial for obtaining reliable and relevant results. Several factors should be considered when selecting a cell line for AML research, such as specific markers and genetic abnormalities associated with different subtypes of AML. It is also essential to evaluate the karyotype and mutational profile of the cell line, as these can influence the behavior and response to the treatment of the cells. In this review, we evaluate immortalized AML cell lines and discuss the issues surrounding them concerning the revised World Health Organization and the French–American–British classifications.

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“…They found that they successfully validated their results in three large-scale screening experiments. In this way, they have proven that XAI will help healthcare providers and drug regulators better understand AI medical decisions (Gimeno et al, 2022). It has been shown that machine learning models and scoring functions that simplify the screened Coulomb and Lennard-Jones interactions between ligands and residues of the target receptor can significantly improve the classification ability to improve the mentioned virtual screening and identify active ligands (Shimazaki & Tachikawa, 2022).…”

Section: Uncertainty Estimationmentioning

confidence: 99%

Explainability and white box in drug discovery

2023

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Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies.This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed.

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Explainable artificial intelligence for precision medicine in acute myeloid leukemia

Cited by 19 publications

References 57 publications

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

Choosing the Right Cell Line for Acute Myeloid Leukemia (AML) Research

Explainability and white box in drug discovery

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