Using Artificial Intelligence (Watson for Oncology) for Treatment Recommendations Amongst Chinese Patients with Lung Cancer: Feasibility Study

[Application of artificial intelligence in thoracic surgery]

BACKGROUND

The application of artificial intelligence is a relatively new option to enable improved patient treatment in modern medicine and is therefore currently the focus of many research projects. In the clinical practice the application of artificial intelligence so far seems to be confined to the analysis of medical imaging.

OBJECTIVE

In which form is the use of artificial intelligence possible in routine daily work in thoracic surgery and is already being practiced?

MATERIAL AND METHODS

A search of the currently available literature was performed.

RESULTS

Under current conditions artificial intelligence can best be used as part of diagnostics and treatment planning; however, in order to enable a comprehensive use standardization and evaluation of the centralized data collection are necessary.

CONCLUSION

At the present time promising study results are available but the implementation into the surgical routine has so far been very difficult.

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

Concordance in postsurgical radioactive iodine therapy recommendations between Watson for Oncology and clinical practice in patients with differentiated thyroid carcinoma

BACKGROUND

To the authors' knowledge, the indications for radioactive iodine (RAI) therapy in patients with differentiated thyroid carcinoma (DTC) are unclear; treatment decisions are based on physician judgment. The objective of the current study was to identify the degree of concordance between postsurgical RAI therapy recommended by Watson for Oncology (WFO), a clinical decision support system for oncological therapy, and that recommended by physicians for patients with DTC.

METHODS

The current retrospective cohort study included 207 patients with DTC who underwent thyroidectomy between 2017 and 2018. Treatment recommendations were considered concordant if WFO rendered recommendations consistent with those of the physicians.

RESULTS

Treatment recommendations were concordant for 160 patients (77%). The concordance rate significantly differed according to the American Thyroid Association (ATA) risk category (P < .001) and American Joint Committee on Cancer TNM stage (seventh edition; P = .004). Logistic regression analysis demonstrated that treatment recommendations were significantly less likely to be concordant in patients with ATA intermediate-risk and stage III disease compared with those with ATA low-risk and stage I disease (odds ratio, 0.16 [P < .001] and OR, 0.35 [P = .004], respectively).

CONCLUSIONS

The authors believe the concordance rate between postsurgical RAI therapy recommendations rendered by WFO and those rendered by physicians was too low to justify adopting WFO for the comprehensive screening of patients with DTC. This is particularly true among patients with ATA intermediate-risk and stage III disease, reflecting differences in practice patterns between the United States (where WFO was calibrated) and Korea. Hence, WFO is not a substitute for physicians, and also may require regional customization to improve its assistive capability.

Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy

Background

Research in embodied artificial intelligence (AI) has increasing clinical relevance for therapeutic applications in mental health services. With innovations ranging from ‘virtual psychotherapists’ to social robots in dementia care and autism disorder, to robots for sexual disorders, artificially intelligent virtual and robotic agents are increasingly taking on high-level therapeutic interventions that used to be offered exclusively by highly trained, skilled health professionals. In order to enable responsible clinical implementation, ethical and social implications of the increasing use of embodied AI in mental health need to be identified and addressed.

Objective

This paper assesses the ethical and social implications of translating embodied AI applications into mental health care across the fields of Psychiatry, Psychology and Psychotherapy. Building on this analysis, it develops a set of preliminary recommendations on how to address ethical and social challenges in current and future applications of embodied AI.

Methods

Based on a thematic literature search and established principles of medical ethics, an analysis of the ethical and social aspects of currently embodied AI applications was conducted across the fields of Psychiatry, Psychology, and Psychotherapy. To enable a comprehensive evaluation, the analysis was structured around the following three steps: assessment of potential benefits; analysis of overarching ethical issues and concerns; discussion of specific ethical and social issues of the interventions.

Results

From an ethical perspective, important benefits of embodied AI applications in mental health include new modes of treatment, opportunities to engage hard-to-reach populations, better patient response, and freeing up time for physicians. Overarching ethical issues and concerns include: harm prevention and various questions of data ethics; a lack of guidance on development of AI applications, their clinical integration and training of health professionals; ‘gaps’ in ethical and regulatory frameworks; the potential for misuse including using the technologies to replace established services, thereby potentially exacerbating existing health inequalities. Specific challenges identified and discussed in the application of embodied AI include: matters of risk-assessment, referrals, and supervision; the need to respect and protect patient autonomy; the role of non-human therapy; transparency in the use of algorithms; and specific concerns regarding long-term effects of these applications on understandings of illness and the human condition.

Conclusions

We argue that embodied AI is a promising approach across the field of mental health; however, further research is needed to address the broader ethical and societal concerns of these technologies to negotiate best research and medical practices in innovative mental health care. We conclude by indicating areas of future research and developing recommendations for high-priority areas in need of concrete ethical guidance.

[An Overview of the Application of Artificial Neural Networks in Lung Cancer Research]

肺癌是目前全世界发病率、死亡率最高的肿瘤，目前的诊疗手段效果有限，精准医学的全面开展为提高肺癌诊疗水平带来了新的契机。但临床医生很难对精准医学需要的多维度多角度的资料（生物组学、临床检测指标以及非生物的环境背景资料等）进行有效的整合和利用，难以为患者选择最优的诊治方案。借助计算机技术的发展，以人工神经网络（artificial neural networks, ANNs）为代表的人工智能具有高容错性、智能性和具有自我学习能力的特点，其强大的信息整合能力可以对精准医学的发展与应用起到很大的帮助，在肺癌的基础研究和临床实践中发挥巨大的作用。本文对肺癌领域ANNs应用的现状进行综述。