Image Captioning is a challenging artificial intelligence problem which refers to the process of generating textual description from an image based on the image contents. A common answer would be "A woman playing a guitar". We as humans can look at a picture and describe whatever it is in it, in an appropriate language. For all of us'non-radiologists', a common answer would be "a chest x-ray". Well, we are not wrong but a radiologist might have some different interpretations.

About the author: Anish Sebastian co-founded Babyscripts in 2013, which has partnered with dozens of health systems for its data-centric model in prenatal care. As the CEO of the startup, Anish has focused his efforts on product and software development, as well as evidence-based validation of their product. Prior to this, he founded a research analytics startup and served as a senior tech consultant at Deloitte. Last month saw the rollout of the latest upgrades to Amazon's Echo speaker line: earbuds, glasses and a ring that connect to Amazon's personal assistant Alexa. These new products are just three examples of a growing trend to incorporate technology seamlessly into our human experience, representing the ever-expanding frontiers for technology that have moved far past the smartphone. These trends and others are going to make a big impact in the healthcare space, especially as providers, payers and consumers alike slowly but surely recognize the need to incorporate tech into their workflows to meet the growing consumer demand for digital health tools.

Do you think we're gonna be replaced?

When it comes to increasing the accuracy of medical diagnoses, reducing worker burnout, and providing cheaper universal healthcare, AI seems like a natural solution. AI appears to have secured a prominent role in the medical industry as both entrepreneurs and policymakers extol the immense potential in incorporating machine learning and deep neural networks into a doctor's daily routine. Decades worth of medical data collected from every appointment, procedure, and survey sit untouched in databases while algorithms wait hungrily for training data. Prominent applications of AI in predictive diagnostics lie in image-based diagnostics and preemptive predictions through machine learning. Amidst the bustling excitement over the applications of AI in healthcare, the medical industry maintains its slow and sluggish pace in adopting new technologies.

Among the oft-asked questions about AI is, "Will it put doctors out of business?" And while the question is of interest to pretty much everyone, no one has a more immediately vested interest in it than doctors themselves, except perhaps medical students still training to become doctors. Not surprisingly, then, AAMC News, a media outlet for the Association of American Medical Colleges, recently ran a long article that looked at the question in depth. "An array of studies have offered glimpses of AI's enormous potential," writer noted early on. From algorithms out-performing radiologists in identifying myriad forms of cancer, to AI detecting rare hereditary diseases in children, to predicting the cognitive decline in Alzheimer's patients, the triumph of AI over human counterparts has been documented far and wide.

Summary: The world of healthcare may look like the most fertile field for AI/ML apps but in practice it's fraught with barriers. These range from cultural differences, to the failure of developers to really understand the environment they are trying to enhance, to regulatory and logical Catch 22s that work against adoption. According to data compiled by research firm Startup Health funding for digital healthcare totaled $14.6 billion in 2018. The world of healthcare may look like the most fertile field for AI/ML apps but in practice it's fraught with barriers. These range from cultural differences, to the failure of developers to really understand the environment they are trying to enhance, to regulatory and logical Catch 22s that work against adoption.

Artificial intelligence--the mimicking of human cognition by computers--was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce.

Radiologists bring home $395,000 each year, on average. In the near future, however, those numbers promise to drop to $0. Don't blame Obamacare, however, or even Trumpcare (whatever that turns out to be), but rather blame the rise of machine learning and its applicability to these two areas of medicine that are heavily focused on pattern matching, a job better done by a machine than a human. This is the argument put forward by Dr. Ziad Obermeyer of Harvard Medical School and Brigham and Women's Hospital and Ezekiel Emanuel, PhD, of the University of Pennsylvania, in an article for the New England Journal of Medicine, one of the medical profession's most prestigious journals. Machine learning will produce big winners and losers in healthcare, according to the authors, with radiologists and pathologists among the biggest losers.

With artificial intelligence poised to take over image-centric medical domains, some health experts are urging radiologists and pathologists to consider merging into a single specialty. Advancements in deep learning have paved the way for computers to take on a bigger role in reading medical images, but that doesn't mean machines will replace radiologists and pathologists entirely, according to a viewpoint published in JAMA. Instead, the authors argue that the two specialties should merge into a single role as "information specialists," allowing computers to take over the menial tasks associated with reading images. Artificial intelligence--including IBM's Watson--can read thousands of X-rays and CT scans in a matter of minutes, identifying fractures or abnormalities far quicker than a radiologist. By adopting a modified role in order to make room for AI advancements, clinicians would oversee machine interpretations and make higher level clinical decisions by integrating information from patient charts.

Artificial intelligence--the mimicking of human cognition by computers--was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce. Radiologists have always revered machines and technology. In 1960, Lusted predicted "an electronic scanner-computer to examine chest photofluorograms, to separate the clearly normal chest films from the abnormal chest films."3