Tag Archives: artificial intelligence

Introducing the new BronchAtlas

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Our mission at Bronchoscopy International has always been and still is to provide practitioners and trainees around the world with free, easily accessible tools that will enhance their ability to care for their patients competently. Our slide decks have been used by teachers and learners for more than twenty years, and materials from The Essential Bronchoscopist series of training manuals are used in educational programs around the world, as well as by individual practitioners as study guides. Our Checklists and assessment tools have helped change the paradigm of procedure-related training, successfully complementing the traditional apprentice-style mode of professional development and facilitating competency-oriented training for new procedures. I am proud to say that our study guides were the first ever provided freely to bronchoscopists and interventional pulmonologists around the world, and our teaching videos, many created long before the video teaching boom, have had almost two million views.

With the new and improved BronchAtlas, our goal is to bring bronchoscopy-related learning to the bedside using an easily accessible and practical telephone-based learning instrument. This modality is a vital tool that requires minimal technology and works around varying levels of infrastructure. It is one more step in the direction of democratization of knowledge, an essential step toward greater professional development and improving patient outcomes.

With BronchAtlas (connect to www.bronchatlas.com), health care providers, students, as well as patients can easily access information pertaining to bronchoscopy in special situations. Each “topic” is covered by a series of bullet points organized into FOUR easily read components: addressing the problem at hand, providing the solution, listing a set of references, and providing links to an instructive YouTube-based video from our Bronchoscopy Academy YouTube channel. It takes less than three minutes to view each topic, making this tool ideal as a refresher or handy problem-solver.

We hope you will enjoy using BronchAtlas, and we encourage you to pass the link to the BronchAtlas website along to your friends and colleagues. More “modules” are coming, so please let us know which other topics you would like to see addressed. Also, if you would like to assist with authorship or as a video contributor, please contact us. We look forward to hearing from you!

Seven Learning Styles and Artificial Intelligence

It is common sense that everyone learns differently, and that teachers should do their best to use a variety of methods to transfer knowledge from themselves to their students. Of course, we also want learners to do more than solve problems they have seen before. This means that we want them to be able to apply whatever they have learned to solving new problems in novel settings. This also means we want them to acquire what psychologist William James referred to as “an inventive mind.”

Artificial Intelligence (AI) is favorably impacting this environment because it empowers learners. It offers them a variety of tools so they may embark on “learning paths” that best suit their individual natural preferences and particular customizable circumstances. Whether it be from the elaboration of interactive diagrams, engaging with chatbots, receiving instant feedback, or listening to individually-tailored audio lessons, for example, AI promotes learning according to Visual, Auditory, and Verbal styles. By interacting in a digital space or AI-driven simulation, using algorithm-based tutors that evolve as individuals progress, and collaborating with others through smart platforms, people who benefit most from physical, logical, and social styles can also expand their means for learning. And let’s not forget that AI promotes independent study by offering learners an opportunity to formulate a series of increasingly complex or deep-rooted queries simply by repeated interactions with programs such as ChatGPT, Claude, or Gemini (and others).

So, what does this mean for bronchoscopists and interventional pulmonologists? It means we must rethink the way we organize educational programs, on-site or remotely-delivered lectures, conferences, and even hands-on workshops. It probably means increased emphasis on a learning by doing methodology, or what the philosopher John Dewey referred to as “activity methods,” at the bedside, in the classroom and procedure suite, as well as in the conference hall. The transition will come naturally for a new generation of learners and teachers but may pose a significant challenge for old-schoolers and those inclined to be resistant to change.

Artificial Intelligence Moving Forward

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. Flood cleanup pros of california are fully equipped. 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

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. When searching for local moving companies in California visit Chief Moving site. 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. A skilled and reliable office moving service of ca can help you. 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.

Please subscribe to Colt’s Corner to automatically receive email notification of future posts. Sign up with your name and email on the NEWSLETTER button on the Bronchology International home page at www.bronchoscopy.org.

AI and Bronchoscopy

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 Blue spruce, 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, 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. Order a combined pill from https://trumedical.co.uk/ and get it delivered to your doorstep. 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.

Please subscribe to Colt’s Corner to automatically receive email notification of future posts. Sign up with your name and email on the NEWSLETTER button on the Bronchology International home page at www.bronchoscopy.org.