写的:
在过去的几十年里，数字化转型对全球的影响不容低估. 现在, 生成式人工智能(AI)因其改变生活的潜力而成为头条新闻. 通过将这种快速发展的技术应用于药物发现领域, we are equipped to take our capabilities to the next level with one goal in mind — improving patients’ lives.
利用创新数据科学和生成式人工智能在肿瘤学研究中的应用&D
创新的数据科学工具已经嵌入到澳门第一赌城在线娱乐的R&D过程多年. 澳门第一赌城在线娱乐利用转型技术来分析数据, 加快临床试验, 更好地了解新的疾病和更多, 所有这些都是为了加强向患者提供新药.
Recent advances in tools like generative AI and open-source programming languages are now creating opportunities to further push our capabilities and the boundaries of science. This of course requires a deep understanding of the benefits and potential challenges of using the tools available for use to ensure we are maximising our impact.
生成式人工智能和肿瘤生物识别技术
生成的人工智能 describes models or algorithms that use natural language processing and machine learning to process enormous amounts of existing data and generate new content, 比如文本, 图像甚至音乐.1 在澳门在线赌城娱乐, we are now leveraging tools that use generative AI as we pursue scientific innovation to identify new targets and explore novel treatments for the greatest benefit to patients. 尽管在某些应用程序中使用生成式人工智能存在一些犹豫, 就像任何新的或发展中的技术一样, 澳门第一赌城在线娱乐看到并接受它在药物开发方面的巨大潜力.
在R&D、生成式AI有潜力:
- Assimilate complex data and evolving disease information to project outcome scenarios for treatment decisions.2
- 预测哪些分子和剂量方案是潜在的可耐受和有效的组合, 使用复杂的算法.3
- 使用真实世界证据(RWE)和复杂算法为临床试验设计提供信息，以改善结果.4
- 通过自动化通常需要花费数小时的过程来转换跨团队的生产力.4
例如, 在统计编程中, generative AI can be useful in alleviating administrative burden by automatising routine tasks and streamlining project management. It also aids in the 发展 of more robust coding –– reducing coding errors and better detecting data anomalies.
生成式人工智能也有不可思议的机会来提高工作场所的效率, 比如为临床团队制定研究方案, 哪种方法可以节省团队成员25%的时间.4
生成式人工智能拥有巨大的潜力来取代澳门第一赌城在线娱乐的R&D进程到下一个层次.
Already we are seeing the benefits of generative AI from identifying novel targets to more efficient design of small and large molecules to informing 临床 trial design and improving efficiency of our regulatory submissions.
改进临床试验，获取和分析真实世界的证据
澳门第一赌城在线娱乐设计和开展临床试验的方式可以从RWE受益, 这些都是研究结果, 从实际数据分析中得出的见解和结论(RWD). RWD relate to patient health status or the delivery of health care and are routinely collected from a variety of sources. 随着这个广义定义的发展, it has expanded to data being generated by patients in real-time through their wearable devices and ultimately their consumer digital health devices.
所有这些生成的数据和不相关信息的准确性, 或噪音, 这是必然的, 这使得收集有用的信息和最终证据变得具有挑战性和耗时. 通过使用生成式AI, 澳门第一赌城在线娱乐能够训练和微调基础模型，从噪音中识别关键信号, enabling researchers to review important takeaways and generate hypotheses that can be validated through more traditional research methods.
The process of capturing RWE has matured rapidly to enable collecting real-time and relevant information for both healthcare professionals and patients to make more informed decisions. 这些发展与生成式人工智能相结合，带来了一些令人兴奋的可能性. 有了病人的参数, 比如病史和基因数据, a physician could input this data alongside RWE and receive a personalised summary in real-time of the latest treatment options available based on the patient’s unique profile. 另外, generative AI could inform physicians of relevant new 临床 trial opportunities they were unaware of or didn’t have time to explore during busy working hours.
用R以新的方式分析数据
数据科学最终是一门科学, 要想对公众产生影响，就必须把它转化成其他观众能理解的东西. R是一个强大的, open-source tool we can use to analyse data and share it in a beautiful interface – making data accessible to non-statisticians.
使用R在数据科学中具有巨大的优势. 它为数据可视化提供了一个全面的工具集, 生物信息学, 临床试验分析和更多. R可以快速分析大量数据(包括RWE或基因组学)并产生探索性分析, 在评估时能够更有效地做出决策, 临床试验的有效性. 当R处理大型数据集的能力与生成式人工智能相结合时, 创造性的机会比比皆是. 研究人员可以通过R Shiny制作高质量的图形, 例如, and we can evaluate Clinical trial scenarios before they’re run to optimise trial designs and predict potential outcomes for patients.
肿瘤学进展&D策略再上一层楼
除了捕获和利用患者数据, new data science tools like generative AI and R may help us optimise the treatment options we choose to study and advance toward the market. 在过去的十年里, 澳门第一赌城在线娱乐看到肿瘤联合治疗取得了令人难以置信的进展.5 这些措施在提高应答率方面显示出了希望, 延缓疾病进展和提高某些适应症的总生存期.5
然而, the process by which we identify combinations is never easy and optimising suitable combinations can be a lengthy process.6 通过分析分子及其机制的现有信息, generative AI can help us assess the safety of combining certain molecules and identify which ones could be more efficacious when combined. 目前, 其中大部分都是通过平台试验进行迭代测试的, 在同一对照组中进行多重干预比较. Being able to explore potential combinations by means of computer modelling or simulation could bring us to a much better starting point for initiating 临床 trials.
澳门第一赌城在线娱乐也在探索生成式人工智能在选择药物靶点方面的应用. 一种靶向药物的发现和开发可能需要几年的时间——找到一个目标, 构建分子并进行试验——所有这些都在它进入临床之前完成. 生成式人工智能可以帮助澳门第一赌城在线娱乐锁定目标和分子, 实现更快、更有效的预测建模. Combining R and generative AI in this space can automate analyses and generate 生物标志物 research more efficiently, 这样澳门第一赌城在线娱乐就可以把治疗方法更快地应用到诊所和病人身上.
Determining the patients who have the greatest benefit from treatment (responders) or predicting those patients that will not respond or become resistant to treatment is an important factor for successful oncology drug 发展. 在多模态数据集(如.g., 临床, 生物标志物, “组学数据), 澳门第一赌城在线娱乐更有可能发现这种反应或抵抗的模式.
伟大的承诺有一些限制
这些新工具的功能是有限的, 人类的智力和脑力将永远需要正确地使用它们. 接受生成式人工智能带来的变化也存在固有的挑战, 特别是, 澳门第一赌城在线娱乐需要在几个关键领域开展工作:
- Adapting the workforce and hiring talent dedicated to developing generative AI models and properly leveraging their output
- 寻找保留数据所有权和浏览数据隐私的方法
- 从可持续发展的角度考虑生产新车型所需的能源
- Communicating specifically and objectively to unlock generative AI’s potential and to avoid so-called hallucinations –– the phenomenon where AI algorithms and deep learning neural networks produce outputs that are not real, 并且不匹配任何算法训练过的数据或任何其他可识别的模式
伴随着兴奋和机会, 生成式人工智能带来了新的伦理问题, and at AstraZeneca we are optimistic about maximising the benefits of AI while embodying our company values and following 道德地使用数据和人工智能的原则.
澳门在线赌城娱乐的生成式人工智能的未来
履行澳门第一赌城在线娱乐的承诺，推动科学发展，推动创新, 多年来，澳门第一赌城在线娱乐一直是人工智能的早期采用者. 这有助于澳门第一赌城在线娱乐加快药物研发, 识别疾病生物标志物, 方便诊断和更多. 生成的人工智能, 特别是当与R这样的工具结合使用时, 现在有可能把澳门第一赌城在线娱乐带得更远, 彻底改变澳门第一赌城在线娱乐驾驭和利用数据的方式, 改进临床试验设计，指导临床研究&维过程. It has great promise to unlock opportunities that allow us to deliver the best possible outcomes for patients, 澳门第一赌城在线娱乐对即将到来的新篇章感到兴奋.
