2. Imagination
A British technology business, Imagination Technologies creates and licenses
intellectual property (IP) for semiconductors for use in consumer electronics,
mobile devices, and other sectors.
Along with other IP solutions, the business also provides multi-core CPUs for
use in AI. [1]
Target market is big companies
Product created utilises Imagination's AI acceleration
3. Agilent
An offspring of Hewlett-Packard, Agilent Technologies specialises in
life sciences, diagnostics and the required safety testing of pharmaceuticals.
Agilent Technolgies offers analytical equipment, software, services and
consumables. Numerous applications such as genomics, clinical research and
drug development, make use of these items. [2]
Target market is scientists, pharmaceutical companies and government.
The product created targets pharmaceutical testing
4. Competitors
IBM Watson Health: IBM Watson Health is developing AI medical systems to help doctors
and researchers make more informed decisions about patient care. [3]
Siemens Healthineers: Siemens Healthineers is developing AI solutions to help with
medical imaging, diagnostics, and therapy. [4]
Philips Healthcare: Philips Healthcare is developing AI systems to improve patient
care and reduce healthcare costs. [5]
Dexcom Glucose monitors: Helps diabetics in managing glucose levels by continuously
monitoring levels. [6]
5. Proposed Solution- Remote Health tracker
VitalScience CAD Rendering
Health tracker monitoring module
Docking station frame
Blood extracting needle
Indicator LED
Slot for dashboard screen
6.
7. Advantages and innovation
There are often barriers that prevent a fully representative sample. [7]
This product will allow people to carry on with their lives – less invasive.
Ability to increase the speed and efficiency of vaccine research, reduce the need
for large-scale clinical trials, and provide more accurate and real-time data
The volume of data produced is a lot more and cheaper to obtain.
The data can be used to better understand how medications work and any possible
side effects.
In the future the data collected can be used for better diagnosing disease. [8]
The needle is replaceable so can be used on different patients.
One of IBM Watson Health's biggest setbacks was the revelation that its cancer
diagnostics tool was not trained with real patient data. [9]
8. Societal Impact
Greater public trust in vaccines
Faster and more accurate vaccinations/medications
Company prosperity will bring more tax money due to more taxable income,
more jobs created, and more opportunities made.
Less burden for hospitals as trial patients don't need constant attention in
hospital.
Could put people out of jobs due to automation
People may worry about privacy due to constant tracking [10]
9. Conclusion
Overall, digital and wireless health check-ups using health monitors can be
a tool for scientists to constantly track individual patients at home whilst
using AI to best analyse the data.
By providing early detection of health problems, improving management of
new medicine/vaccines with their effects and reducing healthcare costs.
This device can help both scientists, with constant data and AI analysis of the
data, and people.
It can cause some ethics issue but with the correct regulation it won't be an
issue
This fulfils the target market of big medical companies
10. Reference list
[1] “AI,” Imagination. https://www.imaginationtech.com/products/ai/ (accessed Feb. 16, 2023).
[2] “About Agilent - Agilent,” www.agilent.com, Nov. 01, 2022. https://www.agilent.com/about/
[3] “IBM Watson Health | AI Healthcare Solutions,” IBM Watson Health, 2019. https://www.ibm.com/watson-health
[4] “Siemens Healthineers | Corporate Home,” www.corporate.siemens-healthineers.com. https://www.siemens-healthineers.com
[5] “Artificial Intelligence,” Philips. https://www.philips.com/a-w/about/artificial-intelligence.html (accessed Feb. 16, 2023).
[6] Dexcom, “Dexcom,” Dexcom, 2018. https://www.dexcom.com/en-GB
[7] “Representation in Clinical Trials: A Review on Reaching Underrepresented Populations in Research,” ACRP, Aug. 10, 2020.
https://acrpnet.org/2020/08/10/representation-in-clinical-trials-a-review-on-reaching-underrepresented-populations-in-research
[8] V. Mayer‐Schönberger and E. Ingelsson, “Big Data and medicine: a big deal?,” Journal of Internal Medicine, vol. 283, no. 5, pp. 418–429,
Jan. 2018, doi: https://doi.org/10.1111/joim.12721.
[9] “Where did IBM go wrong with Watson Health?,” Quartz, Mar. 02, 2022. https://qz.com/2129025/where-did-ibm-go-wrong-with-watson-
health
[10] V. J. Dzau and C. A. Balatbat, “Health and societal implications of medical and technological advances,” Science Translational Medicine,
vol. 10, no. 463, p. eaau4778, Oct. 2018, doi: https://doi.org/10.1126/scitranslmed.aau4778.