Tag Archives: artificial intelligence

Artificial Intelligence Moving Forward

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It took thirty years (1967-1997) for computer chess programs to defeat world champion players, but it was only eight years (2009-2017) before DeepMind’s AlphaGo defeated Ke Jie, the world’s premier Go player. Video games like Starcraft are harder for computers to play than board games such as chess or Go, but after only 18 months of research, Google’s Deepmind utterly destroyed the fastest professional human players (https://www.newscientist.com/article/2191910-deepmind-ai-thrashes-human-professionals-at-video-game-starcraft-ii/).

With such rapid advances in artificial intelligence, it is no wonder we must rethink the medical profession. Image analysis programs are disrupting radiology, dermatology, ophthalmology, and other specialties. Your AppleWatch can monitor for atrial fibrillation and record an electrocardiogram. Deep learning, data-driven decision-making, neuro-fuzzy systems, confabulation, and adaptive resonance theory have widespread applications in healthcare. 

As the role for artificial intelligence increases in day-to-day medical practice, doctors will be more productive. They will read more X-rays, process decision-making algorithms more quickly, and produce probabilistic studies more efficiently for prognosis and case-specific treatment strategies. Also, GPS-type guiding systems and robotics are likely to enhance patient safety, decrease the risk for surgical errors, and increase productivity. Qubits, the quantum version of classic binary bits, are ready to revolutionize computer mechanics (https://www.nature.com/articles/s41586-019-1666-5.pdf). Subsequent increases in computing speed and power will further alter possible applications of AI in a futuristic cyber and robotic world.

It will be a while, however, before AI replaces bronchoscopists, so IP professionals have job security. Still, rethinking our roles as health care professionals is wise and forward-thinking. We are expanding Bronchoscopy International’s successful Train-the-Trainer programs to help instructors enhance their skills teaching decision-making and communication, as well as incorporate novel technologies into learning and teaching processes. By incorporating new competency-oriented educational materials and methodologies, faculty will be even better equipped to inspire colleagues and generations of enthusiastic interventional pulmonologists!

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Deep learning in Radiology and Pathology affects Bronchoscopists

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This is a second post relating to the promising role of artificial intelligence in interventional pulmonology.  My point is that lung specialists will spend less time learning facts and figures that are easily replaced by computer-generated analyses of complex algorithms. Much of this is because of Deep learning

This subset of machine learning (programs that adjust themselves as they are exposed to more data, but without human input) uses artificial neural networks (algorithms built on unstructured data). The word deep is a technical term referring to the number of layers in the neural network. Artificial Neural networks being a set of algorithms modeled after the human brain and used to recognize patterns.  Image recognition is one example, and its principles are responsible for much of the work done today in radiology and pathology. 

For example, using deep learning and pattern recognition, AI reveals CT abnormalities and interprets findings (Google’s AI team recently outperformed traditional radiologists looking at 45,800 screening CTs for lung cancer https://www.fiercebiotech.com/medtech/google-s-cancer-spotting-ai-outperforms-radiologists-reading-lung-ct-scans), and chest radiographs are accurately interpreted using fuzzy logic interpretations of spatial relationships (https://www.ijcaonline.org/specialissues/dia/number1/4156-spe320t).

Pathology is another area where practice patterns will undoubtedly change. In many regions, expert cytologic interpretation of lung and mediastinal nodal specimens is lacking. Digital pathology (image-based information generated from a digital slide) allows real-time interpretation by computers at sites that are distant from wherever the procedure takes place. Humans already do this despite the cost and logistic difficulties.  I believe that artificial intelligence will soon facilitate and universalize the process (https://www.healthimaging.com/topics/artificial-intelligence/ai-lung-cancer-slides-accuracy-pathologists). 

In today’s post, my goal was to introduce the concept of deep learning and provide a few examples of how this mode of artificial intelligence will affect procedural practice by changing how chest radiology and pathology are practiced. Rather than devote study time to learning X-ray and cytology interpretation, future bronchoscopists will improve their abilities to incorporate findings into appropriate management plans, as well as communicate results to patients, caregivers, and health-care teams.

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AI and Bronchoscopy

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This is the first of several posts about the role of artificial intelligence and the future of interventional pulmonology*.  I am confident our field will change immensely in the years ahead, and that artificial intelligence will not only change how we learn and perform procedures but also how we interact with patients. The sooner we embrace these changes, and build partnerships with industry as well as colleagues from other disciplines such as computer engineering, ethics, psychology, philosophy, physics, mathematics, and business administration, the easier it will be to integrate new developments into clinical practice.

Artificial intelligence has many definitions. A quick Google search provides “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” Wikipedia expands on this definition, adding that AI “describes machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem-solving.”

This requires us to familiarize ourselves with the phraseology computer engineers use to describe the learning process, but which is not necessarily foreign to many educators.

From a developmental perspective, AI uses symbolic, connectionist, and other models of learning that are, in fact, similar to how the human brain works. Just as there are several types of knowledge, AI does not rely on only one developmental approach to provide results. This is elegantly explained in a 1990 article by Marvin Minsky (AI magazine, summer 1991), in which he explains how the sentence “ Mary gave Jack the book” prompts the human brain to produce a visual representation of the act, a tactile representation of the experience, a script-sequence of what it means ‘to give’, and various assumptions about Jack, Mary, and the book. Similarly, artificial intelligence must employ not one but several different strategies to provide a result.

Some results are methodology—based on algorithmic and probabilistic approaches. Computer-based interpretation of pulmonary function tests, image-pattern recognition for accurate computed tomography scan interpretation, and patient management protocols based on decision-tree and data-driven statistical algorithms are simple examples of how artificial intelligence brings complex knowledge instantaneously to our fingertips. No longer required to memorize facts and figures, or integrate history/clinical exam/laboratory findings into patterns learned through a prolonged patient-care apprenticeship, doctors will change their practice habits accordingly.

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