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Last updated on 2024年7月23日

How do you optimize AI healthcare application performance?

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AI healthcare applications are transforming the way doctors diagnose, treat, and monitor patients. They can improve accuracy, efficiency, and accessibility of medical services. However, developing and deploying AI healthcare applications also poses many challenges, such as data quality, privacy, security, scalability, and interoperability. How do you optimize AI healthcare application performance to ensure they deliver the best outcomes for patients and providers? Here are some tips to help you.

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1 Choose the right data

Data is the fuel of AI healthcare applications. The quality, quantity, and diversity of data affect the performance of AI models and algorithms. To optimize AI healthcare application performance, you need to choose the right data sources, formats, and methods. You need to ensure that the data is reliable, relevant, representative, and consistent. You also need to clean, preprocess, and label the data properly. Additionally, you need to respect the data privacy and security regulations and standards of the healthcare industry.

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Carrie Atcheson, MD, MPH, FASA

Anesthesiologist | Inclusive Leadership Specialist | Operations Expert
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There is one thing that physicians who do research know about the data gathered on patients, practitioners, and healthcare systems: it is deeply imperfect. Often the most data-rich items are inaccessible to databases (consider a surgical operative report or a discharge summary). These complex documents contain data that exists often nowhere else in the EMR, insurance database, or patient survey. What was found during the operation? Why did the patient stay 6 days instead of 3 in the hospital? Figuring out how the algorithm will access and make meaning of these hidden data points will be the biggest challenge faced by AI applications focusing on decision support and utilization management.

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Reza Shirazi, MD, FACRO, MS,EE

Radiation Oncologist, Physician Governance Board member Unio, former Chair American Cancer Society leadership council San Diego county, Adjunct Professor San Diego State University
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AI holds immense promise in revolutionizing healthcare, and valuable insights can be drawn from the Human Genome Project to unlock this potential: 1. Granular Data Matters: - Diverse Data Types: Beyond genetics and clinical records, encompass lifestyle, socioeconomic, and cultural backgrounds data. - Holistic Patient Views:AI can integrate this diverse data for precise diagnoses and tailored treatments. - Comprehensive Data Handling:** Invest in versatile data collection and handling systems. 2. Incentivizing Data Sharing: - Highlight Benefits:Emphasize improved outcomes, recognition, or monetary rewards for data contributors. - Collaboration Platforms:Foster data exchange through collaborative platforms.

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AC Felix Burden

Retired, now full time carer, continues to peer review
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The data is rarely perfect, so iterative feed back is needed, preferably with help from the owner of the data. An example was low prevalence of CKD, further scrutiny showed the use of several CKD codes in the one practice.

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2 Select the best tools

Tools are the vehicles of AI healthcare applications. The choice of tools can influence the performance of AI models and algorithms. To optimize AI healthcare application performance, you need to select the best tools for your specific needs and goals. You need to consider the features, functions, and compatibility of the tools. You also need to evaluate the costs, benefits, and risks of the tools. Some of the tools you may need include frameworks, libraries, platforms, APIs, and cloud services.

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Reza Shirazi, MD, FACRO, MS,EE

Radiation Oncologist, Physician Governance Board member Unio, former Chair American Cancer Society leadership council San Diego county, Adjunct Professor San Diego State University
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3. User-Friendly AI Interfaces: - Accessibility:Prioritize user-friendly interfaces to make AI insights accessible to non-technical healthcare professionals. - Enhanced Adoption:Intuitive dashboards and decision support systems boost AI adoption. 4. Transparency in AI Algorithms: - Building Trust:Ensure transparency in AI algorithms to promote understanding and trust among healthcare professionals. By implementing these principles, we can harness AI's potential in healthcare effectively, benefiting patients and healthcare providers alike.

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3 Test and validate

Testing and validation are the checkpoints of AI healthcare applications. The results of testing and validation can indicate the performance of AI models and algorithms. To optimize AI healthcare application performance, you need to test and validate your AI models and algorithms regularly and rigorously. You need to use appropriate metrics, methods, and scenarios to measure the performance. You also need to compare the performance with the expectations, requirements, and standards of the healthcare industry.

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Muhammad Rizwan Munawar

Computer Vision, Growth @ ultralytics | visionusecases.com | 120K+ Reads & 250K+ Views on Medium | Open Source Contributor | YOLO11 ?? | Vision Language Models
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The success of AI applications in the healthcare industry is heavily reliant on the level of accuracy the application provides. Several key factors contribute to enhancing this accuracy: a) Ensure the model is consistently validated using real-world data. Relying on auto-generated data or the same dataset used for training may lead to wrong results. b) The initial model deployment may not be the ultimate iteration. continuously optimization will be required based on the model error analysis and feedback from customers over time.

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Thomas Burke, BPharm, MBA

Healthcare Chief Executive Officer ? 25+ Years Driving Complex Nonprofit, Investor-Owned, & Private Equity-Backed Healthcare Systems To Growth, Operational Excellence & National Quality Recognition
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The future of healthcare lies in the effective integration of AI, and the key to harnessing its full potential is through rigorous testing and validation. Ensuring AI models and algorithms perform optimally and without bias requires regular and thorough evaluations using appropriate metrics and methods. This not only aligns with industry standards but also meets the high expectations of delivering quality healthcare to our community.

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4 Monitor and update

Monitoring and updating are the maintenance of AI healthcare applications. The feedback of monitoring and updating can improve the performance of AI models and algorithms. To optimize AI healthcare application performance, you need to monitor and update your AI models and algorithms frequently and proactively. You need to collect and analyze the data, feedback, and outcomes of the AI healthcare applications. You also need to identify and address the issues, gaps, and opportunities of the AI healthcare applications.

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5 Collaborate and integrate

Collaboration and integration are the keys of AI healthcare applications. The level of collaboration and integration can enhance the performance of AI models and algorithms. To optimize AI healthcare application performance, you need to collaborate and integrate with other stakeholders and systems in the healthcare industry. You need to communicate and coordinate with the users, providers, regulators, and partners of the AI healthcare applications. You also need to ensure the interoperability and compatibility of the AI healthcare applications with other systems and platforms.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you optimize AI healthcare application performance?

1

2

3

4

5

6

1 Choose the right data

2 Select the best tools

3 Test and validate

4 Monitor and update

5 Collaborate and integrate

6 Here’s what else to consider

Artificial Intelligence

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How do you optimize AI healthcare application performance?

1

2

3

4

5

6

1 Choose the right data

2 Select the best tools

3 Test and validate

4 Monitor and update

5 Collaborate and integrate

6 Here’s what else to consider

Artificial Intelligence

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