Implications for Medical Data
Overview
We have seen a multitude of examples how artificial intelligence is being used and how it influences society thus far. Two of those major developments tie into this week’s classes. First, image recognition (face recognition) has made significant progress in recent years. Second, big data analysis including unstructured data has made major advancements as well.
These two bring different opportunities and hazards with them.
The range of different data types, its volume, and unreliability make medical data analysis a challenge. As findings are directly connected to the human condition, research and ethics have been aspired to highest standards. Double blind randomized trials are regarded a solid standard for medical research. Analysis of Variance (ANOVA) measures the variance amongst and between samples—another way of quality control to avoid favoring results.
In the medical research field, there is also a stronger focus on understanding sensitivity and specificity of results, a value that indicates the ratio of true negatives to false positives and the ratio of true positives and false negatives, respectively.
Artificial Intelligence in Medical Data
The current stage of AI in health and medicine is to assist doctors and healthcare workers with diagnoses and health issues.
AI in Assisted Medical Imagery
One major field, which benefitted the most is image analysis using machine learning. Figure 1 illustrates the ratio of correctly identified imagery and superiority of computer assisted diagnosis.
Large data sets of skin melanoma allowed for applications using simple smartphone cameras . Professional equipment already surpasses human skills in terms of speed and accuracy. As Figure 1 shows in the turquoise section of discrepancy between the diagnoses, a skilled pathologist can still improve the whole diagnostic process .
AI in Medical Big Data
Big Data in Health is a field that received growing attention . Medical data is a classic in growing data sized with increasing measurement possibilities and various types of data. The review paper (Reading 1) covers Electronic Health Records, Social Health, Lifestyle Environmental Factors, and Public Health as just a few areas. Each discipline has multiple approaches and sub-fields that are the focus of medical data scientists.
There are opportunities for patient management to optimize care and financial support . With easier access to multi-facetted medical big data, such improvements become possible. As analytic models are usually available at low infrastructure cost, they also pose hazards, such as exploitation from employers or insurance companies.
AI in Other Medical Fields
There are other specialized medical fields, such as DNA data mining, protein folding, virology, epidemiology, etc., that have and will see advancements with artificial intelligence.
One of the latest breakthroughs was in the protein folding challenge .
Homework
Theme One
Due Monday, December 12
Study Reading 1, section I. INTRODUCTION and section IV. SENSOR INFORMATICS.
- Prepare in your own words the main takeaway (summary) of section IV., subsections A. Wearable, Implantable, and Ambient Sensors, B. From Sensor Data to Stratified Patient Management, and C. Mobile Health.
Theme Two
Due Thursday, December 15
Study Reading 1, section II. MEDICAL AND HEALTH INFORMATICS and prepare a group presentation. Make sure to study all sections to be able to ask constructive questions for each other’s presentations.
- Prepare a 15 minute group presentation with group members and topics as follows:
- A. Electronic Health Records (EHRs): ONO Taishi, NAKAMURA Soichiro, KAKIUCHI Ai, SAIKO Moeka, HANAOKA Nuttarika
- B. Social Health: IDE Kento, CAI Yuxin, LANCASTER Griffin Forbes, LI Cunpu, ARAI Rika, HIBINO Yuri
- C. Lifestyle Environmental Factors, and Public Health: KUNIMATSU Hanon, LOU Bo, TZU Yun-Hsiang, HAN Seungwoo
Prepare a slide deck (PPT or PDF) to show during your presentation.
References
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