Life sciences professionals want artificial intelligence (AI) tools and they want them now.
在我们最近对300名Life Sciences IM专业人士的调查中，有91％的受访者表示，他们希望“在所有数据管理活动中大大增加对人工智能（AI）的使用”。像几乎所有其他行业一样，这些生命科学专业人员对预测分析和自动数据管理的承诺也很感兴趣。他们很兴奋。
But the survey also highlights that only 40 percent are ‘extremely satisfied’ with the current AI features in their IM solutions.
AI工具有可能改变生命科学公司的信息管理。可以教导机器学习算法（这是AI的核心部分）来执行各种信息管理任务，包括将源文档从一种语言转换为另一种语言，解释非结构化的叙述以及基于经常性模式的预测和建议活动（例如，下一个最好的电话）。
But these aren’t plug-and-play solutions that can be deployed overnight to magically solve all of your data management issues. Machine learning algorithms need to fit the specificities of the industry and then be trained to do these tasks, which requires time, analytics and market expertise, and a lot of clean data to learn from. The larger and more consistent these datasets are, the more accurate the results of the algorithms will be.
这是业务领导者通常不完全理解的AI的方面，可能会引起挫败感。即使有91％的受访者表示他们想使用AI，但只有大约60％的受访者准备这样做。另外46％的人说机器学习是他们最想要的服务 - 但觉得他们在当前产品中找不到。
这表明他们认识到需要机器学习算法使他们的AI梦想成真，但是供应商没有达到他们的期望。
机器人与算法
Part of the problem is that there is often a mismatch between the idea of what AI can possibly do and what the current generation of AI solutions can really deliver. The majority of functionalities currently available still rely on conventional rules-based automation, rather than full machine learning solutions. These rules-based bots can be useful for automating manual tasks, but they aren’t smart enough to decipher what data means, or determine whether something requires human attention.
一些供应商具有更复杂的解决方案，但是由于它们通常来自纯粹的技术公司而不是以生命科学为本的公司，因此他们可能缺乏行业专业知识来理解AI工具的最佳用例。这可能会导致算法不会收集正确的数据，或者以与监管要求不符的方式使用数据，从而使用户处于不符合性的风险。
肮脏的数据困境
不过，数据本身是有效AI驱动分析的最大和最少讨论的障碍之一。公司用来训练算法的许多内部数据库都是凌乱，非结构化的，并且充满了错误。拼写错误，重复数据，缺失字段和不一致的报告策略都导致“肮脏的数据”阻碍了这些工具的有效性能。
This isn’t just a life sciences issue. One经验研究found U.S. organizations across industries believe 32 percent of their data is inaccurate; and 91 percent of respondents believe their revenue is negatively affected by inaccurate data.
肮脏的数据不是故意的问题。当公司合并数据库，依靠不一致的工作流程并无法建立适当的报告规则和维护以保持数据清洁和一致时，它发生在幕后。例如，当未验证名称拼写时，标题不一致，忽略了数据，问题就会堆积起来。
无论出于何种原因，直到清除数据之前，任何算法培训都不会产生准确的结果。幸运的是，公司可以使用AI和机器学习算法解决此问题 - 如果他们能找到合适的供应商来帮助他们做到这一点。
In the short-term, an information management vendor that has access to large global healthcare data sets can provide clients with lots of clean data to train their algorithms, eliminating the need to rely on their own smaller and less consistent databases for these projects. This generates faster and more reliable results from early phase AI and machine learning deployments. It can also help business leaders prove the value of these tools to support further investment in AI innovations.
从长远来看，可以构建算法以清理肮脏的数据，从而帮助公司重置基线以达到数据一致性。通过正确的培训，可以教授算法以查找和消除重复，比较命名拼写，填写丢失或不准确的数据以及标记慢性错误以进行进一步的人进行干预。与任何机器学习项目一样，清洁数据库都需要时间，但是它为所有以未来数据驱动的工作奠定了基础。
如果您想确保您的组织准备基于AI驱动的分析做出业务决策，那么您想知道数据是干净，一致和准确的。
未来的AI策略
There is still a lot of hype in the information management space, so life sciences companies should choose their vendors wisely. The ideal partner for these investments will have proven experience in the life sciences industry, access to large diverse global data sets, and experience building algorithms for specific life sciences use cases. They should also be able to provide a technology demonstrating long term plans to evolve their AI offerings that meet the needs of a life sciences clientele.
The life sciences industry is still in the nascent stages of AI and machine learning for life sciences, but the potential impact is significant. These tools promise to deliver insights that will drive time and cost savings, accelerate drug development, and enhance targeted sales and marketing strategies for better bottom-line results. Choosing the right partner today and investing the time to create clean and consistent data is the best way to accelerate this evolution and build a strong foundation for that AI driven future.
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