5/5 (5)

Advertisers are always trying to find the golden formula to target their customers. Cookies and demographical data is being used online, but due to technological innovations that have made facial recognition possible we are also able to target our customers better outdoors. While waiting for the train, there is a great possibility that a smart billboard is watching you. Are you looking at its advertisement or not? And if so, for how long

Cameras in billboards are becoming more and more common. The software being used can identify the age and sex of a person, identify their emotion and even recognize an individual. Since advertising is all about emotions, it is very useful for the advertiser to identify whether the person looking at the billboard is happy, surprised, angry, etc. They can use this data to investigate their target group and make their advertisements more effective.

Besides offering new insights and data, face recognition in billboards is taken up to the next level and is being used to make billboard advertising interactive. For example, Coca-Cola uses facial recognition in interactive billboards at the metro station in Stockholm. As part of the ‘Choose Happiness’ campaign, billboards analyzed the faces of the metro passengers. When a passenger passed by the billboard and smiled, the face of the interactive billboard advertisement winked and smiled back at the passenger. (Rozema, R. 2016).
Another example is the German beer brand Astra, who is using facial recognition to distinguish men from women. The goal of the outdoor advertisement is to encourage women to drink beer. Depending on the person passing by, one of the 70 available videos is being showed. If the billboard recognized a man or a minor, it asks the person to keep on walking. If it is a female, the billboard shows a fun video (Rozema, R. 2016).

These new technologies open up endless possibilities for outdoor advertising, but privacy must be warranted. The civil rights organization ‘Bits of Freedom’, for example, thinks that facial recognition in billboards used in public transportation is a problem, since it is a public area and passengers cannot avoid the cameras (Schellevis, J.). However, as with many technological developments, there are no clear regulations yet and the law is still behind (Rozema, R. 2016). It is unknown how far legislation for personalized advertisement will go in the future. Are interactive billboards allowed to send a push notification to your smartphone? If billboards are able to identify the person that is coming, are they allowed to register you? Maybe the technology will eventually lead to ultra-targeted advertising: what if you load up thousands of videos of people wearing different clothes and match this data to whatever the person looking at the advertisement is wearing? ‘Beautiful dress you’re wearing. Maybe this jacket will look nice with it; only 20euro at Zara!’

Concluding, technological innovations have enabled advertising companies to analyze images and use this data in order to make their advertisements more effective. Besides that, outdoor advertising is taken to the next level with interactive billboards. How far will these innovations go, without violating privacy?

 

 

 

References:

Rozema, R. (2017). Oprukkende gezichtsherkenning past reclame aan | Marketing trends en nieuws. [online] Available at: https://www.frank.news/2016/02/05/oprukkende-gezichtsherkenning-past-reclame-aan-2/ [Accessed 3 Oct. 2017].

Schellevis, J. (2017). Reclameborden op A’dam CS weten wanneer en hoelang jij kijkt. [online] Available at: https://nos.nl/artikel/2191341-reclameborden-op-a-dam-cs-weten-wanneer-en-hoelang-jij-kijkt.html [Accessed 3 Oct. 2017].

5/5 (5)

Do we need doctors in the future? National Health Service medical director Sir Bruce Keogh believes that doctors will be replaced by computers, since artificial intelligence allows computers to start diagnosing patients with more precision than what was previously possible (Campell, D. 2017). Especially in radiology, IT comes with groundbreaking innovations. By analyzing the huge datasets collected from patients and their symptoms, machine learning can help us to improve diagnoses, choose the right treatment and to execute this treatment (Jacobs, F. 2016).

So what will the advantages of using computers for diagnosing patients?
Diagnostic imaging such as X-ray, CT scan, MRI scan and echography are of high importance for diagnostic measures and treatments. Radiologists analyze the MRI-images with bare eyes and are able to quickly and adequately diagnose the outcome. However, analyses made by human eye are an estimation, there are not quantitative (Jacobs, J. 2016). Due to technological innovations that enable computers to read and analyze diagnostic imaging, analysis can be made quantitative. Artificial intelligence decreases the dependency on incomplete knowledge or memory of the doctor, which decreases human mistakes and makes diagnoses more trustworthy (Vize, R. 2017). A great example of this is the artificial intelligence machine IBM’s Watson that has been used in the hope to diagnose a 60-year-old woman, since doctors weren’t able to figure it out. The machine went through 20 million research papers about cancer and within 10 minutes came up with the correct diagnose as well as suggesting a new treatment for an uncommon form of leukemia (Ng, A. 2016).

Another advantage of artificial intelligence in diagnosing is that small and subtle changes over time are very hard to observe by human eye. When observations are quantitative, the smallest changes will be noticed which enables doctors to adapt daily patient treatment (Jacobs, F. 2016).

Will machines replace radiologists in the future?
The revolution from bare eye analysis to computer-assisted diagnosis is groundbreaking (Jacobs, F. 2016). As a consequence of artificial intelligence systems, will the practicing radiologist be redundant? According to Van Buchem this will not be the case in the upcoming decennia, because there will always be the need for an expert with sufficient knowledge to analyze the complex information (Jacobs, F. 2016). The systems will be more advanced for computer-assisted diagnoses, and not for complete computer-diagnoses. The founder of digital healthcare company Babylon stated; ‘there is no solution which can fundamentally cut the costs of healthcare as long as we are reliant on humans’ (Vize, R. 2017). Since the need for an expert will not decrease, unfortunately healthcare cost will not decrease in the near future.

We can conclude that due to artificial intelligence, diagnoses can be quantitative, which increases accuracy of diagnoses and decreases doctor’s errors. Small changes in the status of the patient can be noticed, by which the doctor can decide to adapt the treatment in an earlier stage than he would be able to do without this quantitative knowledge. The role of doctors will be refined, not replaced. As a consequence, it is unlikely that healthcare cost will decrease in the near future.

 

 

 

References

Campbell, D. (2017). Patients’ illnesses could soon be diagnosed by AI, NHS leaders say. [online] the Guardian. Available at: https://www.theguardian.com/society/2017/sep/12/patients-illnesses-could-soon-be-diagnosed-by-ai-nhs-leaders-say [Accessed 27 Sep. 2017].

Jacobs, F. (2017). Kunstmatige intelligentie verandert beroep van de radioloog – SmartHealth. [online] SmartHealth. Available at: http://www.smarthealth.nl/2016/11/28/philips-kunstmatige-intelligentie-radiologie/ [Accessed 27 Sep. 2017].

Ng, A. and Ng, A. (2017). IBM’s Watson gives proper diagnosis after doctors were stumped. [online] NY Daily News. Available at: http://www.nydailynews.com/news/world/ibm-watson-proper-diagnosis-doctors-stumped-article-1.2741857 [Accessed 27 Sep. 2017].

Vize, R. (2017). Technology could redefine doctor-patient relationship | Richard Vize. [online] the Guardian. Available at: https://www.theguardian.com/healthcare-network/2017/mar/11/artificial-intelligence-nhs-doctor-patient-relationship [Accessed 27 Sep. 2017].