The various schools of medical philosophy in the world hold diagnosis to be the cardinal element of their respective treatment methodologies. A field that was once instinctive and even clairvoyant, it is now an area of vehement research, with the global expenditure on MedTech showing a steady incremental pattern since the beginning of the decade, predicting the same up to 2024. Today, the Health IT industry encompasses a wide range of powerful healthcare technological tools, from EHRs (Electronic Health Records) and patient engagement tools to medical imaging information tools and diagnostics software, while companies today are striving to make viable innovations in the field of remote medical diagnosis due to the perennial threat of Covid-19.
Since the correct diagnosis can be the difference between life and death, the incorporation of IT calls for several prerequisites that ensure the safety of the patients; though they are many, it comes down to the logistics. For instance, a cluttered UI for a diagnostic tool could be counter-intuitive and become a detriment to the data integration due to operational difficulties. The tendency to unreservedly accept data from machines can taint the judgment of even the most experienced medical practitioner, leading to faulty diagnoses. The tools must also act as an enhancement to workflow, which would automate mundane tasks, and expedite tiresome, inefficient procedures, while never increasing physical or cognitive workload.
When the above criteria are fulfilled, IT empowers the medical industry to identify and classify diseases, serve to operate medical equipment or analyze the risk levels of patients towards developing specific diseases. Today the AI revolution has enabled us to look beyond the horizon and witness the endless possibilities it offers for the medical industry.
While some are merely self-explanatory, it is worth discussing some of the new trends in this seminal field.
AI image integration
The diagnostic scene utilizes a handful of scanning and imaging tools for investigation, most of which are not helpful in diagnosis by themselves in more complicated cases of trauma. For instance, a routine bone contusion, usually self-managed, may also imply micro trauma to the local soft tissues which may lead to pain and exacerbations in inflammation. While an X-Ray can help diagnose the bone injury, the muscle damage is discernable only through CT-scan probing. For a holistic treatment program to be synthesized, a juxtaposed interpretation of the two images would be key; it is here that the power of sensor fusion can be harnessed to combine the data. To enable efficient analysis of patient scans, image recognition AI software should be able to combine and interpret data from different imaging sources to gain a better perspective of the patient’s pathology. This could generate concise insights into disease/injury progression, providing radiologists with a higher level of understanding of the condition of patients.
While we can implement software to make critical diagnoses for a certain pathology, trends today show that this can be scaled to work for multiple disease diagnosis systems as well.
It is important to note that the technology at hand is more than commendable; the latest studies show that a neural network can be trained to diagnose a given pathology faster than the average radiologist. Yet, the standard software and equipment employed by most healthcare and diagnostics services may be bypassed by rare disease/disorder morphologies, which are ultimately misconstrued by the algorithms and thereby leading to misdiagnoses. Optimistic predictions for the future signal the formulation of neural networks that can deduce a plethora of conditions from a single image (or any other kind of data set); this would be a huge step in diagnosing rare conditions like genetic and autoimmune disorders. Though the process for such diagnosis would be arduous, requiring a superlative level of skill in radiologists, it is projected that such broad-spectrum software will soon replace the single pathology algorithms, owing to their greater applicability and financial viability in the long run.
“Neutral” algorithms for compatibility
The introduction of new AI software in medical diagnostics facilities is viewed as turbulence in the hospital’s routine workflow, thereby cast as a deterrent to their productivity(a significant reason for low adoption rates), since the installation and setup may become a difficult task. This is why companies today are making concerted efforts to develop universally compatible software that can be fed into the radiologists’ set up with minimal hassles. This is becoming increasingly common today as most all FDA-cleared algorithms are vendor-neutral, meaning they can be set up in scanners and devices across brands and models.
Today, giant strides have been made in implementing this software directly into diagnostic devices like scanners (MRI, CT-scanner), which can help the automation of medical imaging using RPA.
This has been achieved, for example, with MaxQ AI’s Intracranial Hemorrhage (ICH) technology being embedded into Philips’ Computed Tomography Systems. The potential downside is that employees have little to no freedom of choice in the software they use; if the embedded software does not meet their requirements, they must opt for cloud-based software. However, the financial edge of software-embedded devices cannot be overlooked, as the machines are more efficient and can bolster the financial returns that come with them.
As we move towards a future where AI model architecture becomes less convoluted, and ironclad data security systems with HIPAA-compliant technology streamlining the safe access to patients’ medical data, the implementation of relevant technology would be the factor that dictates the success or subsistence for diagnostic service companies.
Even at the individual level, one can utilize remote diagnosis technology, which is becoming increasingly popular in providing primary diagnoses to ensure timely interventions and treatment.