Category: Artificial Intelligence

Hey Watson, Can I Sue You?

Currently, three South Korean medical institutions – Gachon University Gil Medical Center, Pusan National University Hospital and Konyang University Hospital – have implemented IBM’s Watson for Oncology artificial intelligence (AI) system. As IBM touts the Watson for Oncology AI’s to “[i]dentify, evaluate and compare treatment options” by understanding the longitudinal medical record and applying its training to each unique patient, questions regarding the status and liability of these AI machines have arisen.

Given its ability to interpret data and present treatment options (along with relevant justifications), AI represents an interim step between a diagnostic tool and colleague in medical settings. Using philosophical and legal concepts, this article explores whether AI’s ability to adapt and learn means that it has the capacity to reason and whether this means that AI should be considered a legal person.

Through this exploration, the authors conclude that medical AI such as Watson for Oncology should be given a unique legal status akin to personhood to reflect its current and potential role in the medical decision-making process. They analogize the role of IBM’s AI to those of medical residents and argue that liability for wrongful diagnoses should be generally based on a medical malpractice basis rather than through products liability or vicarious liability. Finally, they differentiate medical AI from AI used in other products, such as self-driving cars.

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Artificial Intelligence in Healthcare: Lessons from Finance

“We built it and we just let it run. We’re a few dudes in an office and our goal is to keep it running. It does everything we could do, except it’s significantly more powerful and it has completely automated how our work is being done,” casually said the hedge fund manager as he described the process by which nearly $1billion was being managed within his fund.

The ‘it’ is an artificial intelligence (AI) based algorithm that uses complex statistics to analyze variables that went into successful decisions and uses advanced computer programs to keep replicating those decisions. All this, while it continuously learns from – and improves upon – its mistakes as it encounters new variables.

These machine intelligent systems are applying the many different forms of AI and fundamentally changing the financial industry. From applying Natural Language Processing in detecting Anti-Money Laundering and fraudulent financial activity to applying Cognitive Computing to analyze wide varieties of variables in building better trading algorithms and to leveraging Deep Learning to looking at consumer decision patterns and providing personalized ‘chatbots,’ AI is transforming the financial sector.

One of the most noticeable areas where this disruption is taking place is within hedge funds: hedge funds that are transitioning their trading desks to AI backed systems, are already beginning to outperform hedge-funds backed by humans alone. What’s really quite astonishing though is how, in the short span of a few years, how far reaching the results have been.

Hearing about hedgies working with AI researchers to make even more money doesn’t inspire the rest of us to greatness. However, it may be valuable to look a brief historical overview of how the financial industry reached this juncture.

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Five Radiology Artificial Intelligence Companies That Somebody Should Build and Invest In

I’ve previously written comprehensively on where to invest in Radiology AI, and how to beat the hype curve precipice the field is entering. For those that haven’t read my previous blog, my one line summary is essentially this:

“Choose companies with a narrow focus on clinically valid use cases with large data sets, who are engaged with regulations and haven’t over-hyped themselves …”

The problem is… hardly any investment opportunities in Radiology AI like this actually exist, especially in the UK. I thought it’s about time I wrote down my ideas for what I’d actually build (if I had the funding), or what companies I would advise VC’s to invest in (if they existed).

Surprisingly, none of the companies actually interpret medical images – I’ll explain why at the end!

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Could Artificial Intelligence Destroy Radiology by Litigation Claims?

We’ve all heard the big philosophical arguments and debate between rockstar entrepreneurs and genius academics – but have we stopped to think exactly how the AI revolution will play out on our own turf?

At RSNA this year I posed the same question to everyone I spoke to: What if radiology AI gets into the wrong hands? Judging by the way the crowds voted with their feet by packing out every lecture on AI, radiologists would certainly seem to be very aware of the looming seismic shift in the profession – but I wanted to know if anyone was considering the potential side effects, the unintended consequences of unleashing such a disruptive technology into the clinical realm?

While I’m very excited about the prospect and potential of algorithmic augmentation in radiological practice, I’m also a little nervous about more malevolent parties using it for predatory financial gains.

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