When you ask most people why the development of AI in healthcare is not happening at the rate that everybody wants, they would answer that it is because the algorithms have not matured yet. That’s no longer true.
The deep learning software solutions are now genuinely capable of processing scans, performing risk stratification, and detecting patterns invisible to the naked human eye. What makes the situation so difficult is the whole environment surrounding this process, which includes not only the complicated data related to the patients but also the laws and those physicians unwilling to learn how to interact with the new system.
This is the stage at which the majority of healthcare AI ventures go under. This is precisely why an AI infrastructure company gets paid.
What’s Actually Working Right Now
Strip away the hype and a handful of applications are genuinely delivering results in hospitals and public health agencies today:
- Reading scans: The neural network can spot potential problems in X-ray scans, mammograms, skin lesions and retinal images at par with radiologists. And it becomes extremely important where there is lack of radiologists.
- Detecting an outbreak before it even happens: Feed a predictive analytics system with enough clinical data and information regarding the environment and other health parameters and the system will be able to detect an impending outbreak several days or weeks in advance of the usual detection.
- Risk detection in real time: Decision support systems that passively monitor patient’s physiological data and history and alert clinicians when something does not seem right.
- Accelerating drug discovery: Machine learning solutions are cutting years off preclinical trials, which is essential especially in cases where no research funding is available.
- Adapting treatment: Genomic profiling, which has only been a matter of scientific articles until now, is beginning to influence clinical decisions.
This is not about theory anymore; this is where the funding is flowing right now.
Why Good Models Still Fail in the Real World?
What’s not really talked about when an algorithm achieves 95 percent accuracy in testing? It doesn’t really matter anymore once it goes up against real-world clinical data. EHRs are inconsistent; different formats, missing fields, typos, systems that don’t interoperate. The algorithm that was trained on perfect data won’t stand a chance once it encounters the messy reality of clinical work.
And then there’s the regulatory part, which has its own schedule irrespective of how quickly technology evolves. And even the best algorithm technologically is doomed to fail if it disrupts a nurse’s shift, or simply does not fit the clinical practice of scheduling patients.
The other silent threat is bias. When the training data that will help to construct a model is limited to a certain type of patients, then the model may make the exact same erroneous decisions as a biased human-driven system does, but much quicker and without any sense of subjectivity.
Both types of outcomes can be observed in actual deployments of machine learning in the field. Some sepsis prediction systems have helped to save lives and reduce the duration of patient stay in the hospital. On the other hand, there were some famous expensive artificial intelligence tools for the health industry that failed to deliver when tested in reality. The difference usually lies not in the algorithm itself but in the approach of the developers.
A Few Honest Questions to Ask Before You Buy
If you’re from a hospital system, medtech company, or public health organization trying out AI vendors, let’s start off by asking these questions before delving into all the technical details of model architecture:
- Is this product trained on data as messy as yours – not on a neat and tidy benchmark dataset?
- Is there an actual strategy in place to get past compliance and regulatory review (HIPAA, GDPR, etc.)?
- Will this really integrate into the way your team works, or will it require more clicks than current tools?
- Did this product see sufficient diversity in training data to cover the patients you serve?
- Is anyone monitoring this product after it’s released, and what happens when the model drifts?
These are the questions that separate a flashy demo from machine learning solutions that are still running, and still trusted, two years after go-live.
How We Think About This at PSSPL?
Deep learning software solutions are developed under the premise that one day or another, they will have to interact with dirty data, because they will. The projects for which we develop health care AI are in the area of medical imaging analysis, clinical decision support, and disease prediction, and no project starts without taking into account the basics: privacy and regulation, and usability.
It’s not just in healthcare where the importance of this field is evident. The very same strategy – sound feature engineering, scalable deployment, and proactive monitoring to prevent costly issues before they happen – is present in the machine learning applications we develop for other sectors as well, such as predictive maintenance, fraud detection, and computer vision. If you want to see where machine learning is delivering real results outside of healthcare, our roundup of machine learning use cases walks through several of them.
Where This Leaves Us
Deep learning in healthcare was never really about the algorithm being clever enough. It’s about whether the people building it respected how genuinely complicated hospitals, clinics, and public health systems are — and built accordingly. Get that right, and the payoff isn’t just efficiency. It’s care that reaches more people, sooner, with fewer blind spots.
That’s the standard we hold ourselves to on every project, healthcare or otherwise.
Thinking about your own AI project? Talk to Prakash Software Solutions Pvt. Ltd.’s AI and ML engineering team about what it would actually take to get it into production.