1 00:00:00,050 --> 00:00:00,740 Case study. 2 00:00:00,770 --> 00:00:05,240 Continuous improvement and reliability in AI driven medical diagnostics. 3 00:00:05,240 --> 00:00:06,830 The Med Smart case study. 4 00:00:06,860 --> 00:00:13,190 Achieving continuous improvement and reliability in AI systems requires a strategic approach that addresses 5 00:00:13,190 --> 00:00:17,270 the rapid pace of technological change and evolving data landscapes. 6 00:00:17,810 --> 00:00:23,390 Consider the case of Med Smart, a company specializing in AI driven medical diagnostics. 7 00:00:23,630 --> 00:00:29,180 When launching their new AI system, the primary objective was to enhance diagnostic accuracy and patient 8 00:00:29,210 --> 00:00:30,050 outcomes. 9 00:00:30,530 --> 00:00:36,620 Doctor Susan Clark, the lead data scientist, and her team were responsible for ensuring the AI model 10 00:00:36,620 --> 00:00:40,760 remained effective and aligned with medical standards over time. 11 00:00:41,450 --> 00:00:47,480 The first task was to establish a baseline performance metric for the AI system by analyzing historical 12 00:00:47,480 --> 00:00:48,290 patient data. 13 00:00:48,320 --> 00:00:54,800 The team identified key performance indicators such as sensitivity, specificity, and false positive 14 00:00:54,800 --> 00:00:55,460 rates. 15 00:00:56,060 --> 00:01:01,100 These metrics served as benchmarks for evaluating the system's future performance. 16 00:01:01,550 --> 00:01:07,070 How can the effectiveness of baseline performance metrics be ensured across different AI applications? 17 00:01:07,550 --> 00:01:13,130 By understanding that each domain has its unique requirements, the team tailored their metrics to reflect 18 00:01:13,130 --> 00:01:16,100 the critical aspects of medical diagnostics. 19 00:01:16,520 --> 00:01:22,820 For example, sensitivity and specificity were prioritized over user satisfaction, a metric more relevant 20 00:01:22,820 --> 00:01:24,470 to recommendation systems. 21 00:01:25,460 --> 00:01:30,800 To maintain the AI system's performance, robust data management strategies were implemented. 22 00:01:31,310 --> 00:01:37,490 Med smart adopted an iterative process for collecting, validating, and integrating new data into their 23 00:01:37,490 --> 00:01:38,840 training data sets. 24 00:01:39,050 --> 00:01:43,550 The dynamic nature of medical data meant that the system had to adapt continually. 25 00:01:43,820 --> 00:01:48,290 What challenges might arise from integrating new data into existing AI models? 26 00:01:48,710 --> 00:01:54,770 Data drift, where statistical properties of input data change over time was a significant concern. 27 00:01:55,070 --> 00:02:00,790 Through rigorous validation processes, ensuring the new data was clean, relevant and representative. 28 00:02:00,820 --> 00:02:03,040 The team mitigated these risks. 29 00:02:03,970 --> 00:02:08,740 Comprehensive monitoring and logging systems were crucial for real time performance tracking. 30 00:02:09,070 --> 00:02:15,610 Med smarts monitoring tools captured system responses, error rates, and resource utilization metrics. 31 00:02:15,640 --> 00:02:20,410 How can effective monitoring and logging systems help in maintaining AI performance? 32 00:02:21,220 --> 00:02:27,490 These tools provided transparent insights into the AI systems behavior, enabling timely interventions 33 00:02:27,490 --> 00:02:29,230 when anomalies were detected. 34 00:02:29,830 --> 00:02:36,040 Detailed logs of operations, including input data processing steps and outputs, were invaluable for 35 00:02:36,040 --> 00:02:38,620 diagnosing issues and conducting audits. 36 00:02:39,280 --> 00:02:45,640 Regular audits formed the backbone of Med Smarts Continuous evaluation framework conducted by independent 37 00:02:45,670 --> 00:02:49,420 teams comprising domain experts, data scientists, and ethicists. 38 00:02:49,450 --> 00:02:54,070 These audits ensured compliance with medical standards and regulatory requirements. 39 00:02:54,550 --> 00:03:01,480 What role do audits play in the long term reliability of AI systems audits helped identify biases, 40 00:03:01,480 --> 00:03:07,510 ethical concerns, and security vulnerabilities, providing opportunities to refine objectives and performance 41 00:03:07,510 --> 00:03:09,700 metrics based on new insights. 42 00:03:11,200 --> 00:03:16,390 Frequent updates were necessary to keep pace with changes in medical knowledge and innovations. 43 00:03:16,930 --> 00:03:23,770 Netsmart categorized updates into model updates involving retraining the AI with new data and system 44 00:03:23,800 --> 00:03:27,430 updates, which included infrastructure and algorithm enhancements. 45 00:03:27,910 --> 00:03:33,670 How can the balance between model updates and system updates be managed to ensure seamless integration? 46 00:03:34,210 --> 00:03:39,670 Automation of model updates ensured scalability, while thorough testing in controlled environments 47 00:03:39,670 --> 00:03:43,630 minimize the risk of introducing errors during system updates. 48 00:03:45,400 --> 00:03:49,270 Stakeholder engagement was a vital aspect of smart strategy. 49 00:03:49,780 --> 00:03:56,040 The company sought feedback from end users, regulatory bodies and the public through surveys, Consultations 50 00:03:56,040 --> 00:03:57,150 and workshops. 51 00:03:57,570 --> 00:04:02,160 Why is stakeholder engagement essential for AI system development and maintenance? 52 00:04:02,760 --> 00:04:08,700 Feedback from diverse perspectives ensured the AI system remained user friendly and aligned with societal 53 00:04:08,700 --> 00:04:09,510 values. 54 00:04:10,110 --> 00:04:15,570 Domain experts provided insights that augmented technical evaluations, ensuring the system's relevance 55 00:04:15,570 --> 00:04:16,530 and efficacy. 56 00:04:17,730 --> 00:04:20,820 Comprehensive documentation was another critical area. 57 00:04:20,820 --> 00:04:26,700 Med smart ensured that every aspect of the AI system, from design and development to deployment and 58 00:04:26,700 --> 00:04:29,010 maintenance, was thoroughly documented. 59 00:04:30,210 --> 00:04:35,280 How does documentation contribute to the sustainability and evolution of AI systems? 60 00:04:35,550 --> 00:04:41,310 Detailed records facilitated knowledge transfer within teams, supported audits, and provided references 61 00:04:41,310 --> 00:04:43,650 for troubleshooting and future development. 62 00:04:44,370 --> 00:04:50,220 Well-maintained documentation ensured transparent and traceable evolution of the AI system, crucial 63 00:04:50,220 --> 00:04:51,900 for accountability and trust. 64 00:04:53,130 --> 00:04:57,780 Ethical considerations were integrated at every stage of med smarts AI life cycle. 65 00:04:58,380 --> 00:05:03,960 The company established ethical guidelines, conducted regular ethical audits, and promoted a culture 66 00:05:03,960 --> 00:05:06,600 of responsibility among AI practitioners. 67 00:05:07,350 --> 00:05:11,550 How can ethical frameworks be effectively integrated into the AI life cycle? 68 00:05:12,000 --> 00:05:18,150 By proactively addressing ethical concerns, Massmart ensured the AI system did not perpetuate biases 69 00:05:18,150 --> 00:05:22,980 or inequalities, building trust and acceptance among users and stakeholders. 70 00:05:24,120 --> 00:05:30,630 The process of preparing AI systems for continuous evaluation and updates involves multiple facets. 71 00:05:30,960 --> 00:05:37,020 Establishing baseline metrics requires an understanding of the domain specific needs and potential impacts. 72 00:05:37,320 --> 00:05:43,470 Robust data management strategies ensure continuous adaptation to new data while mitigating risks like 73 00:05:43,470 --> 00:05:44,520 data drift. 74 00:05:44,580 --> 00:05:50,850 Effective monitoring and logging provide transparency and facilitate timely interventions, while regular 75 00:05:50,850 --> 00:05:55,200 audits identify areas for improvement and ensure compliance with standards. 76 00:05:55,500 --> 00:06:01,650 Frequent updates categorized into model and system updates are essential for maintaining relevance and 77 00:06:01,650 --> 00:06:02,520 performance. 78 00:06:03,420 --> 00:06:08,580 Engaging stakeholders provides valuable feedback that enhances the system's alignment with societal 79 00:06:08,580 --> 00:06:09,360 values. 80 00:06:09,780 --> 00:06:15,690 Comprehensive documentation supports sustained evolution and accountability, while ethical considerations 81 00:06:15,690 --> 00:06:18,150 ensure responsible AI practices. 82 00:06:19,620 --> 00:06:26,070 By adopting these strategies, Massmart successfully maintained their AI systems performance and reliability, 83 00:06:26,100 --> 00:06:31,890 demonstrating how continuous evaluation and updates can navigate the complexities of the AI landscape. 84 00:06:32,610 --> 00:06:38,010 These practices not only enhance the system's technical robustness, but also build trust and alignment 85 00:06:38,010 --> 00:06:42,900 with societal values, ensuring sustainable and responsible AI deployment. 86 00:06:44,490 --> 00:06:45,660 References. 87 00:06:46,110 --> 00:06:46,800 Sal. 88 00:06:50,790 --> 00:06:51,690 Paz.