AI如何重塑医学成像业务

几家医院和医疗机构已经将人工智能作为他们十年长的临床使用困境的急需解决方案。无论使用哪个术语，AI，深度学习，机器学习或人工神经网络，该技术已经开始改变医疗保健全景，并像以前从未有任何可能的效率。工作流程是自动化的，患者和医生的总体满意度已大大提高。

In the current scenario, most healthcare agencies are battling with the problem of an overload of unstructured and disorganized patient data. Physical structuring and mining of such data is a daunting task that can rarely be managed efficiently. One large component of this data is medical imaging data. Further, clinicians are struggling with sorting this data out to identify the relevant or actionable information. The issue is further compounded due to increasing patient volumes, reimbursement procedures, bundled payment systems, and the shift from fee-for-service to a fee-for-value reimbursement system. AI is the answer. They can be easily structured and mined throughElectronic Medical Record(EMR) technologies.

医学成像中AI转换的范围

Data gathering and mining has limitless possibilities in the world of medical imaging when enabled through AI. Recently, a new dimension of data has been introduced, called bidirectional patient portals, wherein patients are permitted to submit their own data and images into their EMRs. This will further reduce the costs of in-person clinic encounters and follow-ups. The features can also include activity tracking, physical monitoring, etc. which was previously limited to clinic visits.

AIhas the scope to:

“The world market for machine learning in medical imaging, comprising software for automated detection, quantification, decision support and diagnosis, is set for a period of robust growth and is forecast to top $2 billion by 2023”, according to a new report from Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare technology industry.

这个世界市场份额AI-based medical imaginghas been predicted to be bifurcated in the following manner:

The Importance of AI Transformation in Medical Imaging:

The impact ofAI医学成像can be viewed as one that will create a virtual assistant for every medical imaging employee that acts in a perfectionist fashion. The predictive analysis technology can make great strides in increasing the overall efficiency of imaging equipment utilization. Additionally, the data mining feature of the technology is capable of offering immediateclinical decision support toward diagnosis through the meaningful interpretation of medical images. There is a further component of AI-related technologies called ‘Adaptive Intelligence’, which pulls related historical information of the patient in an automated fashion, allowing the clinician or doctor a comprehensive view of the patient’s health. Finally, tumor assessment features include automated quantification and side-by-side comparisons of tumor assessments.

There are several advantages of enabling AI-technologies in medical imaging, below are a few:

诊断成像程序的数量不断增加，该国的放射科医生数量减少，增加了对AI采用的需求。通过深度学习技术基于AI的医学图像分析is at an all-time high.

Criticism of AI in Medical Imaging:

The advantages of AI for medical imaging are numerous. However, there is some hesitancy in its adoption due to the early nature of the technology and the barriers they pose. The following hindrances will need to be addressed before this technology is whole-heartedly accepted:

监管的过程是一个具有挑战性的一个there are very few products available that have been approved and commercialized, specifically the deep learning-based algorithms.

放射科医生仍未对机器学习软件的准确性充满信心，并且正在等待增加调查和研究以评估其功效。

访问卓越质量数据的高成本和间接费用，为智能开发模型analytics, pattern identification, training of algorithms, etc. are still considered a challenge when weighed against the value.

AI-based image analysis systems need to integrated into the working systems of radiologists and be available from the initiation of the process.

Due to the scattered nature of the software, many radiologists find that their models are incompatible with others. This causes more damage than good due to inefficient electronic medical record-keeping and a lack of comprehensiveness.

Due to security reasons, many of the systems are kept away from the internet, making information sharing and access of data impossible. It defeats the entire purpose of electronic information since it is not easily accessible.

Many radiologists are wary about these technologies since they are unsure of their reading.

执行医学成像中AI转换项目的步骤：

Considering that AI-based software is still in its nascent stage, until recently, medical image analysis software was stationary in nature and required a mandatory one-time software license. With the evolution of technology and基于云的解决方案, there are varying subscription and fee-per-study models that are becoming available in the market.

即使大多数医学成像公司都对软件即服务（SaaS）模型表示偏爱，但有些人仍然是永久许可结构的偏爱。前者提供较低的前期成本，因为不需要购买软件许可证，但是从长远来看，后来的工作更便宜。这导致了这样一个事实，较小的公司更喜欢订阅模型，而较大的公司选择了一次性许可系统。预算最适合基于音量的定价模型。

Step 1:The first step in the process is to familiarize the medical imaging company with the benefits and capabilities of AI. This will create a fair idea of the specific requirements of the company and the relevance ofAI满足这些特定需求。

Step 2:一旦公司熟悉AI的功能，下一步就是确定您的个人组织的差距。对需要解决的领域的分析将使专注于AI旨在解决的问题。应从放射科医生的特定需求方面清楚地看到AI的价值。

步骤3：The next order of business is to focus on the business priorities and conduct an analysis that weighs the value of adopting AI with relation to the cost of implementation involved. Every investment should be directly tied to the business value it brings to the company as a whole.

第4步：Create provisions among the employees and radiologists to familiarize themselves with the available technology and participate in offering suggestions towards AI-adoption. Very often, they can point out to hindrances and advantages that may have been overlooked. This is because they are deeply involved in the everyday workings of the company.

步骤5：Once the groundwork has been established, it is time to identify the developers that offer the specific solutions and set up a pilot project. Establishing clear timelines for the pilot projects and the subsequent executions is crucial to success.

步骤6：分配一小组人团队，他们会定期教育有关成型系统的个人以及他们所提供的优势。以定期的方式这样做可以使放射科医生和行政人员在近距离和定期间隔开始使用系统。

Step 7:Take gradual steps toward AI transformation rather than a complete revolution. This will assist the radiologists and administrative staff to gradually adopt and familiarize themselves with changing systems. A sudden drastic transformation may seem overwhelming for the organization.

Step 8:存储，无论是患者数据还是智能算法，都是AI采用的重要组成部分。每个寻求迈向AI的医疗组织都需要为存储要求做出充分的安排。

Step 9:与涉及展示正在进行的工作以及已经采用的技术的开发人员进行定期审查。这些评论确保部署的AI符合原始协议并提供最初设想的价值。

步骤10：保持开发的AI系统与技术能力之间的平衡。这将消除失望的风险，在此过程中，该机构后来发现该技术不符合其预期。

Conducting AI Transformation of Legacy Systems for Medical Imaging:

Every medical imaging organization needs to undertake an evaluation the need of their specific organization, in relation to its legacy system. While a migration is a simple shift of the system to a higher platform, a migration is an upgrade of functionality. A transformation is a complete revamp of the legacy system, in that very little of the original component remains and most of the system is evolved to a considerably higher standard.

Conclusion – The Future of Medical Imaging:

The diagnostic imaging industry is heading for a tremendous revolution, namely AI disruption. With this technology, the industry is set to witness an upscale of productivity, accuracy and a more customized approach for customer. Overall, an improved clinical outcome will be provided, through the adaptation of AI with the consistently increasing amount of medical imaging procedures, compounded by the shortage of radiologists.

Considering that medical imaging holds the largest reservoir of information with regard to patient health, it is ripe for AI adoption. No matter how skilled the radiologist, the volume of data from X-rays, CAT Scans, MRIs and other image tests pose a challenge to their everyday workings. AI has proven its sustainable value through every one of the above-mentioned avenues.

References:

https://www.signifyresearch.net/medical-imaging/ai-medical-imaging-top-2-billion-2023/