Within every aspect of healthcare, time is considered the most valuable component. Even minutes of delay can result in the loss of a life. Early diagnosis lies at the heart of healing patients and timely execution of treatment is of primary importance. At an average, doctors spend 15 minutes with each patient, which when considered intently, is grossly insufficient in providing a comprehensive diagnosis of the illness. In an ideal situation, a diagnosis should be made after careful consideration of all relevant patient information, including similar cases and demographics.
随着医疗保健行业逐渐朝着AI驱动的世界发展，以前被认为是障碍或不太可能的事情现在是相当简单的任务。多年来，该县超过90％的医院已从纸质系统转移到电子流程。在医学诊断方面，患者的记录至关重要。可以通过预测分析来捕获关键疾病的风险，从而节省生命和成本。早期诊断不再是遥远的希望，而是可以通过高级系统轻松实现的现实。
医学诊断中的AI转化是什么？
In a nutshell, adoption of technology that will assist the process of medical diagnosis through automation, prediction, etc. is referred to asAI transformation in medical diagnosis。最近，许多技术公司正在寻求创建减少测试和治疗之间时间的系统。这是通过对医疗记录的自动化和快速采矿来完成的，并有建议的治疗结果。此外，还开发了一些预测分析平台，它使用机器学习来预测死亡率。在这项技术中，有一些功能使医生保持患者行为的循环，例如可能会跳过预约并且对药物不规则的患者，入院的可能性，住院风险等。AI技术也允许患者很容易与医生接触新症状。通过机器学习和高级算法，可以以以前认为不可能的速率检测到条件。
According to a 2016 study by Frost and Sullivan, AI in healthcare:
- 到2021年，目标达到66亿美元
- A 40% growth rate over the next 2 years
- 医学成像诊断将大大改善
- Medical outcomes improvement potential at 30% – 40%
- Costs of treatment improvement potential at 50%
How Important is AI Transformation in Medical Diagnosis?
为医生和内科医生提供技术that allows them to tap into the collective knowledge of other doctors and millions of patient records is a luxury no healthcare organization would refuse. With the advanced systems out there, access to these records is fully automated (based on relevance) and is undertaken within seconds. The potential of such a technology cannot be undermined and this is just one of the many features that AI brings to medical diagnosis. It is like a virtual personal assistant that offers doctors recommendations based on similar cases and the treatment provided.
Although artificial intelligence doesn’t have the ability to replace doctors and physicians, it has the capability of mining data, performinganalyticsand identifying patterns that is fairly impossible for humans to execute. When these technologies are used by skilled physicians and doctors, the medical diagnosis process is multiplied ten-fold quality-wise.
Among the many advantages, the following stand out most distinctly:
- Improved diagnosis
- Reduced costs
- Pattern identification
- 临床相关，高质量和快速的数据生成
The Other Side of the Coin – Criticism of AI for Medical Diagnosis
In spite of the many apparent advantages ofAI for medical diagnosis，在这些技术的执行中，有些担忧是最前沿的。以下是一些在AI的采用时引起犹豫的障碍：
- The high costs involved in accessing superior quality data, developing models for intelligent analytics, pattern identification, training of algorithms, etc.
- Due to the scattered nature of the software, many healthcare agencies find that their models are incompatible with others. This causes more damage than good due to inefficient电子病历-keeping and a lack of comprehensiveness.
- 由于安全原因，许多系统都远离互联网，使信息共享和数据的访问不可能。由于不容易访问电子信息的全部目的。
- Many medical practitioners are wary about these technologies since they view them as an encroachment into a doctor’s turf. The recommendations of these technologies might take doctors away from their instinctive diagnosis, which is their area of skill.
根据思科的说法，“虽然有54％的受访者对医疗保健决策者的最近调查有关AI in healthcareexpect widespread adoption of AI within the next five years, 36% see a lack of trust in AI among patients, and 30% among clinicians, as a barrier to adoption.”
AI在医学诊断中的潜力：
医生和医院越来越倾向于智能系统和过程，以识别有肾脏衰竭，心脏疾病，治疗后感染和医院再入院风险的患者。Electronic health data, compounded by public database information serve as a powerful resource towards diagnosis and suggested treatments. Medication suggestions are provided based on popularity among similar case histories, success rate is determined through patient outcome records, and a plethora of research can now be made available within seconds.
Furthermore, certain medications are ineffective on certain people, and AI systems carry the potential of highlighting this to the doctor at the time of prescription. The analytical systems are also capable of catching anomalies inpatient records。For example, if a patient claims that they don’t consume alcohol, but they display signs otherwise, the system can catch this and highlight it to the relevant doctor.
According to Cisco, “A 2018 study in the Annals of Oncology compared a convolutional neural network (CNN), or machine learning (ML) system, with the determinations of 58 dermatologists. Using more than 100,000 images of malignant and benign tumors, the artificial intelligence (AI) system detected 95% of melanomas accurately, while human dermatologists found 86%.”
To further comprehend the uses of AI, below is an industry-wise breakdown of the uses of AI in medical diagnosis:
Through a concept called deep learning, these intelligent machines, use a wide range of sample data to form algorithms that are meaningful in their approach. The automated nature of these machines allows them to create analysis that is impossible for the human brain to process. Hence, these machines work extremely efficiently to assist doctors and physicians.
Are You Ready to Execute an AI Transformation Project? 10 Steps to Get You There:
Step 1:The first step in the process is to familiarize the healthcare agency with the benefits and capabilities of AI. This will create a fair idea of the specific requirements of the agency and the relevance of AI for those specific needs.
第2步：一旦该机构熟悉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。根据医疗机构的特定需求，应清楚地看到AI的价值。
Step 3:The next order of business is to focus on the business priorities and conduct an analysis that weighs the value of adoptingAIwith relation to the cost of implementation involved. Every investment should be directly tied to the business value it brings to the agency.
Step 4:Create provisions among the employees, nurses and physicians 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 agency.
步骤5:一旦建立了基础工作，就该确定提供特定解决方案并建立试点项目的开发人员了。为试点项目建立明确的时间表，并随后的执行对成功至关重要。
Step 6:Assign a small team of people that will periodically educate the healthcare agency on the systems being incorporated and the manners in which they can be used, along with the advantages they provide. Doing so in a periodic fashion allows the doctors and administrative staff to start using systems at close and regular intervals.
步骤7：Take gradual steps toward AI transformation rather than a complete revolution. This will assist the doctors, nurses and administrative staff to gradually adopt and familiarize themselves with changing systems. A sudden drastic transformation may seem overwhelming for the organization.
步骤8：大的age, whether it is of patient data or intelligent algorithms, is an important component of AI adoption. Every healthcare organization that is looking to make the move towards AI needs to make ample arrangements for storage requirements.
Step 9:Conduct periodic reviews with the developers that involve a display of the work in progress and the technologies already adopted. These reviews ensure that the AI being deployed is in line with the original agreement and providing the value that was originally envisioned.
Step 10:Maintain the balance between the developing AI systems and the capabilities of the technology. This will eliminate the risk of disappointments, wherein the agency later discovers that the technology is not living up to what it was intended for.
Assisting Healthcare Agency to Conduct an AI Transformation of Legacy Systems:
每个医疗保健组织都需要评估其特定组织的需求，并与其遗产系统有关。关于旧系统的解决方案，有不同的术语在周围漂浮，最常见的术语是“迁移”，“现代化”和“转型”。让我们看一下每一个之间的区别：
Migration:This is one of the simplest moves that a healthcare organization can make to their legacy system. It essentially involves moving the system from one platform to another, generally more effective. The functionality and design of the system remains the same and generally the speed, cost, etc. is improvised.
现代化：This is one step ahead of migration and involves an enhancement of the capabilities and functions of the system. This does not involve moving the legacy system to a different platform and the capabilities are modernized and upgraded. The functionality is improvised.
转型：这是一个涉及迁移和现代化的过程。AI转换涉及对传统系统的完整改建，其中最多落在系统的一小部分。平台已更改，功能和功能得到了增强。
结论 - 医学诊断的未来：
The future looks very promising when it is viewed in line with the AI capabilities toward medical diagnosis. Whether it is machine learning, intelligent algorithms, automated data capabilities, andpredictive analysis, there are several avenues of AI that can considerably boost the medical diagnosis process.
