1 00:00:00,050 --> 00:00:00,770 Case study. 2 00:00:00,770 --> 00:00:06,440 Balancing accuracy and interpretability in AI metastasis journey in healthcare diagnostics. 3 00:00:06,470 --> 00:00:12,380 AI model selection is crucial for achieving the delicate balance between accuracy and interpretability, 4 00:00:12,380 --> 00:00:16,310 influencing the effectiveness and acceptance of AI systems. 5 00:00:16,970 --> 00:00:22,400 In this case study, we explore the experiences of Medistat, a healthcare analytics company, as it 6 00:00:22,430 --> 00:00:28,730 tackles the interplay between these two critical dimensions while developing an AI diagnostic tool. 7 00:00:29,930 --> 00:00:36,680 Doctor Emily Carter, chief data scientist at Medistat, faces a pivotal decision her team is developing 8 00:00:36,680 --> 00:00:41,420 an AI model to predict the likelihood of patients developing type two diabetes. 9 00:00:41,720 --> 00:00:47,030 The goal is to provide early diagnosis and intervention, potentially improving patient outcomes. 10 00:00:47,510 --> 00:00:53,510 The challenge is to choose between a highly accurate but opaque deep learning model and a more interpretable 11 00:00:53,510 --> 00:00:56,720 but slightly less accurate logistic regression model. 12 00:00:58,100 --> 00:01:03,360 As the team analyzes data from various electronic health records, they find that the deep learning 13 00:01:03,360 --> 00:01:09,960 model achieves a 95% accuracy rate in predicting diabetes onset, while the logistic regression model 14 00:01:09,960 --> 00:01:12,390 achieves an 88% accuracy rate. 15 00:01:13,050 --> 00:01:16,740 Doctor Carter must decide which model to recommend for deployment. 16 00:01:17,580 --> 00:01:23,280 This decision brings up the first critical question how important is the marginal increase in accuracy 17 00:01:23,280 --> 00:01:27,330 compared to the need for interpretability in the health care context? 18 00:01:29,130 --> 00:01:34,500 The team knows that health care professionals, including doctors and nurses, must understand how AI 19 00:01:34,500 --> 00:01:37,950 models make predictions to trust and effectively use them. 20 00:01:38,490 --> 00:01:40,650 This requirement leads to another question. 21 00:01:41,310 --> 00:01:46,950 Can the complex deep learning model be simplified or made interpretable enough to meet regulatory and 22 00:01:46,950 --> 00:01:48,180 ethical standards? 23 00:01:48,930 --> 00:01:54,930 The regulatory environment in health care demands transparency, especially when decisions impact patient 24 00:01:54,930 --> 00:01:55,500 care. 25 00:01:56,310 --> 00:02:02,730 Doctor Carter ponders if tools like local interpretable, model agnostic explanations or Shapley additive 26 00:02:02,730 --> 00:02:07,970 explanations could be used to provide sufficient transparency for the deep learning model. 27 00:02:09,320 --> 00:02:15,380 While considering this, another aspect surfaces the potential biases within the model. 28 00:02:15,950 --> 00:02:21,500 The team needs to ensure that the chosen model does not perpetuate biases inherent in the training data, 29 00:02:21,530 --> 00:02:28,100 prompting the question what measures can be taken to identify and mitigate biases in the chosen AI model? 30 00:02:28,850 --> 00:02:34,250 Doctor Carter recalls studies showing that models trained on biased data can lead to unfair treatment 31 00:02:34,250 --> 00:02:39,410 of certain patient groups, affecting their trust, and the overall efficacy of the AI system. 32 00:02:40,520 --> 00:02:46,490 To explore these concerns, the team deploys both models in a pilot phase across two hospitals. 33 00:02:47,090 --> 00:02:52,130 They collect feedback from clinicians and patients, revealing that while the deep learning model's 34 00:02:52,130 --> 00:02:58,220 predictions are highly accurate, clinicians are hesitant to act on them due to a lack of clear rationale 35 00:02:58,250 --> 00:02:59,630 behind the predictions. 36 00:03:00,380 --> 00:03:07,190 This feedback emphasizes the importance of transparency and leads to another key question how can user 37 00:03:07,190 --> 00:03:13,940 feedback be effectively incorporated into the model selection process to ensure both accuracy and interpretability 38 00:03:13,940 --> 00:03:15,560 are adequately balanced? 39 00:03:17,450 --> 00:03:22,850 In parallel, the team also evaluates the performance of the logistic regression model in real world 40 00:03:22,850 --> 00:03:23,750 scenarios. 41 00:03:24,230 --> 00:03:29,960 Although less accurate, it provides clear insights into how different features such as age, BMI, 42 00:03:29,960 --> 00:03:32,900 and blood sugar levels contribute to the prediction. 43 00:03:33,380 --> 00:03:39,980 This clarity boosts clinician confidence, supporting patient trust and adherence to recommended interventions. 44 00:03:40,580 --> 00:03:46,670 The pilot results suggest that despite the lower accuracy, the logistic regression model might be more 45 00:03:46,670 --> 00:03:50,150 practical for real world application in this context. 46 00:03:51,470 --> 00:03:57,260 To further analyze the tradeoffs, the team examines cases where the logistic regression model made 47 00:03:57,260 --> 00:03:58,790 incorrect predictions. 48 00:03:59,330 --> 00:04:05,360 They find that in many instances, the errors were due to the model's inability to capture complex interactions 49 00:04:05,360 --> 00:04:06,650 between features. 50 00:04:06,920 --> 00:04:11,960 This finding raises another important question can the accuracy of the logistic regression model be 51 00:04:11,960 --> 00:04:18,500 improved by incorporating non-linear relationships without significantly compromising interpretability? 52 00:04:21,350 --> 00:04:27,290 Doctor Carter considers generalized additive models, which extend logistic regression by allowing non-linear 53 00:04:27,320 --> 00:04:30,230 relationships while maintaining interpretability. 54 00:04:31,250 --> 00:04:37,520 By experimenting with Gams, the team achieves an accuracy rate of 92%, a notable improvement over 55 00:04:37,520 --> 00:04:39,260 the logistic regression model. 56 00:04:40,250 --> 00:04:45,680 The Gams also provide clearer insights into the influence of each feature, striking a better balance 57 00:04:45,680 --> 00:04:47,960 between accuracy and interpretability. 58 00:04:49,430 --> 00:04:54,290 With these findings, Doctor Carter prepares a detailed report for the board, highlighting the trade 59 00:04:54,320 --> 00:04:59,090 offs and recommending the adoption of Gams for their AI diagnostic tool. 60 00:04:59,480 --> 00:05:05,840 The board, comprising clinicians, data scientists and AI governance professionals, reviews the report. 61 00:05:06,230 --> 00:05:12,110 They deliberate on another essential question what additional steps should be taken to ensure the selected 62 00:05:12,110 --> 00:05:15,680 model remains transparent and robust over time? 63 00:05:17,480 --> 00:05:22,880 The discussion centers around implementing continuous monitoring and updating of the model to ensure 64 00:05:22,880 --> 00:05:25,730 it adapts to new data and maintains fairness. 65 00:05:26,090 --> 00:05:32,150 They agreed to set up a dedicated team for ongoing evaluation, and to use model agnostic interpretability 66 00:05:32,180 --> 00:05:36,500 tools to periodically reassess the model's decision making process. 67 00:05:36,920 --> 00:05:43,130 This proactive approach ensures the model remains both accurate and interpretable, aligning with regulatory 68 00:05:43,130 --> 00:05:45,470 standards and ethical considerations. 69 00:05:47,570 --> 00:05:53,960 In conclusion, Metastatic Journey underscores the complex interplay between accuracy and interpretability 70 00:05:53,960 --> 00:05:59,480 in AI model selection by piloting both the deep learning and logistic regression models. 71 00:05:59,510 --> 00:06:04,820 They gather valuable insights that inform their decision to adopt generalized additive models. 72 00:06:05,270 --> 00:06:11,000 This choice offers a balanced solution enhancing patient outcomes while fostering trust and compliance 73 00:06:11,000 --> 00:06:12,710 among health care professionals. 74 00:06:14,930 --> 00:06:21,050 The analysis reveals that prioritizing interpretability in high stakes domains like healthcare is crucial 75 00:06:21,050 --> 00:06:24,710 for gaining user trust and meeting regulatory requirements. 76 00:06:25,520 --> 00:06:31,130 Techniques such as lime and shape, combined with inherently interpretable models like Gams, provide 77 00:06:31,130 --> 00:06:33,950 practical solutions to address these challenges. 78 00:06:34,520 --> 00:06:40,160 Continuous monitoring and iterative improvement further ensure that the AI system remains transparent, 79 00:06:40,160 --> 00:06:42,380 fair, and effective over time. 80 00:06:43,910 --> 00:06:48,680 By reflecting on the thought provoking questions posed throughout the process, students can better 81 00:06:48,680 --> 00:06:54,980 understand how to navigate the trade offs between accuracy and interpretability in AI model selection. 82 00:06:55,490 --> 00:07:01,520 This case study illustrates the importance of considering context, regulatory requirements, and stakeholder 83 00:07:01,520 --> 00:07:07,880 needs when developing AI systems, ultimately guiding professionals toward responsible and impactful 84 00:07:07,910 --> 00:07:09,110 AI deployment.