By ALEX LOGSDON, MD

Leave your bias aside and take a look into the healthcare future with me. No, artificial intelligence, augmented intelligence and machine learning will not replace the radiologist. It will allow clinicians to.

The year is 2035 (plus or minus 5 years), the world is waking up after a few years of economic hardship and maybe even some dreaded stagflation. This is an important accelerant to where we are going, economic hardship, because it will destroy most radiology AI startups that have thrived on quantitative easing polices and excessive liquidity of the last decade creating a bubble in this space. When the bubble pops, few small to midsize AI companies will survive but the ones who remain will consolidate and reap the rewards. This will almost certainly be big tech who can purchase assets/algorithms across a wide breadth of radiology and integrate/standardize them better than anyone. When the burst happens some of the best algorithms for pulmonary embolism, stroke, knee MRI, intracranial hemorrhage etc. etc. will become available to consolidate, on the “cheap”.

Hospitals can now purchase AI equipment that is highly effective both in cost and function, and its only getting better for them. It doesn’t make sense to do so now but soon it will. Consolidation in healthcare has led to greater purchasing power from groups and hospitals. The “roads and bridges” that would be needed to connect such systems are being built and deals will soon be struck with GE, Google, IBM etc., powerhouse hundred-billion-dollar companies, that will provide AI cloud-based services. RadPartners is already starting to provide natural language processing and imaging data to partners; that’s right, you speak into the Dictaphone and it is recorded, synced with the image you dictated, processed with everyone else to find all the commonalities in descriptors to eventually replace you. It is like the transcriptionists ghost of the past has come back to haunt us and no one cried for them. Prices will be competitive, and adoption will be fast, much faster than most believe.

Now we have some patients who arrive for imaging, as outpatients, ER visits, inpatients; it does not matter the premise is the same. Ms. Jones has chest pain, elevated d-dimer, history of Lupus anti-coagulant and left femoral DVT. Likely her chart has already been analyzed by a cloud-based AI (merlonintelligence.com/intelligent-screening/) and the probability of her having a PE is high, this is relayed to the clinician (PA, NP, MD, DO) and the study is ordered. She’s sent for a CT angiogram PE protocol imaging study. This is important to understand because there will be no role for the radiologist at this level. The recommendation for imaging will be a machine learning algorithm based off more data and papers than any one radiologist could ever read; and it will be instantaneous and fluid. Correct studies will be recommended and “incorrectly” ordered studies will need justifications without radiologist validation.