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第一次发帖，违规请删。
我现在在和几个教授出版一个文献集，现在可以帮大家推荐文章，如果有需要，特别是要办NIW的同学，我可以帮你使劲。不是高级刊物，但是可以做引用。文献集关于
XAI in Healthcare: A Proposal for the Series Biomedical and Robotics Healthcare-Explainable Artificial Intelligence (XAI) in Healthcare。

注明：本人于出版社没有任何关系，是真心希望大家投稿，投稿后不是一定出版，是需要review的！

This book highlights the use of Explainable Artificial Intelligence (XAI) for Healthcare computational intelligence systems and techniques to improve the performance and sustainability of areas for healthcare. The theme will be helpful for the researcher and many other users of these technologies.

Call for chapters Letter：大家有兴趣可以投稿。有问题请私信，投了后请私信一下，论坛不允许贴个人联系方式。
Explainable Artificial Intelligence (XAI) in Healthcare
(Edited by Utku Kose, Nilgun Sengoz, Xi Chen, Jose Antonio Marmolejo Saucedo)
to be published by CRC Press
Machine learning, the development of systems that learn from data to recognize patterns and make accurate predictions about future events, has great potential to transform healthcare. Machine learning-based tools can support complex clinical decision-making and automate many routine tasks that can waste healthcare professionals' time and cause job dissatisfaction. Issues with data security and privacy, poor performance of mathematical models or high accuracy models acting as black-boxes, difficulty integrating tools into the workflow, low acceptability of machine learning-based solutions among healthcare professionals, and uncertainty about how to evaluate these solutions There may be barriers to its adoption.
The word black-box mentioned here can be semantically defined as a lack of observability. Since algorithms with a large number of hidden layers such as deep learning networks have black-box models, they create a lack of explanation and trust to the end user.
Developing machine learning-based solutions for clinical purposes requires a solid understanding of clinical care, image processing, data science and application science. A solution design phase for the development of machine learning-based models and user-friendly tools to define the development and adoption of machine learning-based solutions, and an implementation and evaluation phase for the deployment of the solution, an evaluation of the solution and its impact are required. In artificial intelligence (AI), many "data-driven" approaches, and deep learning in particular, suffer from a lack of explainability. They make good predictions in terms of performance accuracy but cannot explain them. In this context, physicians need to understand the recommendations of decision support systems in order to adhere to them. For this reason, Explainable Artificial Intelligence (XAI) is emerging as a new trend in the field of artificial intelligence to overcome such problems. Especially in the health sector, which is one of the critical decision-making areas, the adaptation of artificial intelligence has always been one step behind due to the lack of trust.
This book will introduce use of Explainable Artificial Intelligence (XAI) systems for healthcare field just not for engineers also for clinicians who want to use artificial intelligence for detecting and diagnose the disease effectively. So, this book will appeal to everyone in a broad sense.
Submission Guidelines:
All submissions should be done to: utkukose@gmail.com and / or nilgunsengoz@gmail.com   
Please contact utkukose@gmail.com and / or nilgunsengoz@gmail.com for receiving the chapter preparation documents.
All papers must be original and not simultaneously submitted to another book project, journal or conference. The following important dates will be considered for the submissions:
•        Proposal Submission: 2 April 2023
•        Full Chapter Submission: 28 May 2023
•        Notification of Accept / Reject: 7 June 2023
•        Final Chapter Due: 9 July 2023
Important: Similarity Rate for the full chapter should be max. 15%. The authors able to get similarity report are suggested to send the similarity reports with other files.  
List of Topics (as not limited to):
        Fundamental concepts of Explainable AI
        Transparency interventions in black-box/opaque systems in biomedical problems
        Simulatability, Decomposability, and Algorithmic transparency for biomedical
        XAI Techniques/Frameworks/Tools
        Interpretable Machine Learning in biomedical applications
        XAI for Deep Learning in biomedical applications
        Evaluation Methods and Metrics for XAI
        XAI for disease diagnosis
        XAI for medical treatment processes
        XAI for drug discovery
        IoHT / medical environments with XAI support
        XAI for medical robotics
        XAI for massive data control (i.e. pandemics),
        Usability evaluation of XAI in biomedical applications,
        Human-compatibility with XAI in biomedical problems,
        Anxieties in XAI for biomedical,
        Open problems in XAI for biomedical applications,
        Ethical, Legal and Social Issues of XAI in Healthcare
        Future perspectives in XAI for biomedical applications.
        ...etc.
Draft Chapters:
Some annotated draft table of contents are as follows:
        Chapter 1. Fundamental concepts of Explainable AI: Generate high stakes for any field, a comprehensive explanation of the Artificial Intelligence model with facts and reasoning is crucial. Explainable AI (XAI) should offer understandable explanations to the partners and stakeholders of a user. The explanation accuracy metrics can help engineers and clinicians to emphasize the accuracy of the explanations to their stakeholders
        Chapter 2. Transparency interventions in black-box/opaque systems in biomedical problems: Although artificial intelligence algorithms perform at a high rate in special and critical issues such as health, the issue of transparency has started to be discussed a lot today due to the black-box models they have. In this section, these issues will be discussed in detail.
        Chapter 3. Simulatability, Decomposability, and Algorithmic transparency for biomedical: In this section there will be underlined the XAI systems can be considered transparent, because has the ability of simulatability (being simulated or thought about strictly by a human), decomposability (explaining each of the parts of the method), and algorithmic transparency (the user can understand the process followed by the method to produce any given output from its input data). This is especially important in health, to trust the behavior of intelligent systems.
        Chapter 4. XAI Techniques/Frameworks/Tools: In today's technology, new methods of artificial intelligence are being discovered day by day. However, at the level of explainability, a more innovative approach is required in the implementation of these methods. In this context, innovative approaches to all XAI methods and user-friendly tools will be introduced in this section.
        Chapter 5. Interpretable Machine Learning in biomedical applications: Machine learning is a sub-branch of artificial intelligence. In this context, the focus will be on interpretable models rather than explainable models. In this section, which will provide an overview of the algorithms of machine learning, interpretable methods related to the biomedical field will be emphasized.
        Chapter 6. XAI for Deep Learning in biomedical applications: Although deep learning algorithms give better results than machine learning algorithms in terms of performance, they have the lowest system in terms of explainability. In this context, the level of explainability of deep learning algorithms is essential for the biomedical sector. This section will focus on these issues.
        Chapter 7. Evaluation Methods and Metrics for XAI: Evaluation methods and metrics with which we can compare the success rates of artificial intelligence algorithms are very critical. In this context, it is important to evaluate these evaluation performances as a new scale, especially in the health sector.
        Chapter 8. XAI for disease diagnosis: Although making a diagnosis with artificial intelligence means very important, it is necessary to explain to clinicians why and how this diagnosis is made at an explainable level. In this context, this issue will be discussed.
        Chapter 9. XAI for medical treatment processes: Artificial intelligence is a powerful tool in the effective management of treatment processes as well as in diagnosis. In this context, models will be developed that will enable the management of an explainable and meaningful treatment process for patients with early diagnosis. In this section, this topic will be explained in detail.
        Chapter 10. XAI for drug discovery: Artificial intelligence provides a groundbreaking opportunity for the discovery of new drugs, as in every field. In this section, the content will be presented at an explainable level, that is, at the point of why and how which drug was discovered, and will be discussed on this subject.
        Chapter 11. IoHT / medical environments with XAI support: In this section, how the processes will be handled in the changing medical sector with the integration of XAI methods into IoHT systems will be discussed and focused on.
        Chapter 12. XAI for medical robotics: Robotic systems now find a place for themselves in the medical sector as well as in all areas of our lives. The applications of XAI methods on robotics application areas in the medical sector will be discussed in this section.
        Chapter 13. XAI for massive data control (i.e. pandemics): In the pandemic process we have faced in the past years, we have better understood that artificial intelligence supported systems have shown that we can act more quickly in diagnosing and starting the diagnosis. In this context, more effective management of these processes will be discussed at the level of explainability in this section.
        Chapter 14. Usability evaluation of XAI in biomedical applications: Although XAI systems have already found a new place in the articles, their usability levels and the disappearance of the differences between the researcher and the clinician in this context will be discussed in this section.
        Chapter 15. Human-compatibility with XAI in biomedical problems: Although engineers detect diseases faster and more effectively by achieving high performance with the dataset in their hands, clinicians in particular need to be prepared for this process and manage it well in the context of the medical sector. In this context, this section will focus on this issue.
        Chapter 16. Anxieties in XAI for biomedical: With each new method, there will be some concerns with the XAI method as well. What are their concerns about this process and what they think about this issue will be discussed in this section.
        Chapter 17. Open problems in XAI for biomedical applications: The problems faced by the XAI method and some of the problems faced by clinicians will be evaluated in the context of this topic.
        Chapter 18. Ethical, Legal and Social Issues of XAI in Healthcare: Where the XAI system should be socially, morally and legally and what problems it solves or causes problems will be discussed in this section.
        Chapter 19. Future perspectives in XAI for biomedical applications: In this section, where we will look from a broad perspective that enables XAI methods to adapt to the new era, new methods will be emphasized.
        etc…

谢谢楼主， 我们有个学习小组， 用NLP技术研究了一些疾病， 投了 health care machine learning 相关的会议。 想请教这种可以投你们的期刊集吗？

lz您好，我有一篇biomedical/clinical nlp的高引文章，算是Pioneer Work，希望可以加入你们的文献集，不知道怎么联系您？感谢

我们还在筹划中，马上回有Call to submit letter，感谢大家的关注，请等待update。

这个论坛不能30分钟后edit。我回来私信给你。

我有一篇发表在npj digital medicine上的paper感觉符合你们的要求，希望能考虑加入进文献集。期待你们的update

请教，如果是没有发表过的文章可以投过去试试吗？

补充内容 (2023-02-13 11:27 +8:00):
其实是毕设，之前也没想着发表，但是方向跟这个文献集关注的方向感觉还挺契合的

怎麼聯絡你呢？

怎么联系楼主呢

你好，有几篇关于robot assisted rehabilitation 的已经发表的文章，请问如何联系