Hematology and chemistry were the first to use technologies like algorithms and robotics. It was back in 1984 that the Rutgers University developed a program named EXPERT, which was a consultation system-building tool. This means that it was a knowledge-based AI (artificial intelligence) tool. It enabled sequential lab testing as well as interpreted the results of the same.
These days, AI technologies are commonly referred to as knowledge engineering. AI is basically intelligent computer software that is full of knowledge, and as a result it is capable of playing the role of expert systems. Over the years, experts have found it very hard to develop practical applications using automatic learning. This is the reason why these expert systems often do not have the ability to learn on their own despite these systems being capable of making decisions on the basis of the knowledge that they already have. This is the reason why they are regarded so commonly as AI systems. There are plenty of publications that have been brought out in the domain of AI.
What have they shown?
They show that there is greater interest in the domain of AI as far as healthcare is concerned, and its scope of application is always going up as well. In fact, at sites such as PubMed, you would be able to see around 83,000 publications on the subject of AI in the context of healthcare in the last few years.
Increase in volume of patient healthcare data
It is expected that with the remarkable increase in the volume of data related to patient healthcare, paucity of resources, and a consistent increase in expectations of patients, AI would play a major role.
A driver of growth
It could be the driver of growth of the whole healthcare industry as such. Doctors would soon be adapting to AI and using it in their day-to-day work as well. Healthcare workers such as nurses would be backed by this technology, and they would thus be able to provide a higher level of care and that too to a bigger volume of people in the days to come.
Advances in pathology
It is expected that AI would drive developments in pathology and that too at a rapid rate as any. One can say that there would be paradigm shifts in areas such as digital pathology, precision medicine, and next-gen sequencing, and personalized treatments. This also means that pathologists would be the first people who would be making clinical decisions. Computational pathology could become a lot more important in this particular context. It basically includes the likes of computational models along with the likes of machine learning as well as visualizations. The main aim here is to make the lab output a lot more useful and capable of being understood easily by the clinical decision makers. Computational pathology has clinical value in each aspect of medicine. It normally focuses on computational methods, which use clinical pathology, genomic or molecular pathology data sets, and anatomic pathology inclusive of digital imaging.
Continued remote sensing of patients
This is also expected to be a benefit of using AI in this particular domain. From now on patients would be monitored from remote locations with the help of wearables such as the following:
- glucose monitoring devices;
- oximetry devices;
- temperature devices;
- heart-rate devices; and
- respiratory-rate monitors.
All these would be connected to a central computing device by way of technology known as IoT (Internet of Things).
The norm in the future
In fact, such omnipresent technology is expected to become the norm in the days to come. AI is also expected to help bring about ambient computing that would revolutionize the very way patient care would be provided in the days to come. For example, with the help of AI, doctors would find it easier to predict situations such as sepsis. This is one condition where early detection can be life changing but a bit hard to achieve as such. Several entities in this domain have also tested and come up with predictors that work on the basis of machine learning and are capable of reducing in hospital mortality rate and hospital stay.
In most of these studies, researchers have shown how superior AI-based algorithmic predictors are compared to the electronic health records maintained by the hospitals and healthcare units that were covered in the same. As has been said already, AI has especially proved to be effective in the context of sepsis.
The future of healthcare
Experts feel that in general AI is the future of healthcare. These days, these neural networks have progressed to a high extent and there is great promise and interest in them as well. A number of experts have done studies that have shown the kind of work that AI is capable of doing in this particular regard. For example, AI is much better than the best pulmonary pathologists when it comes to classifying lung carcinoma into various histologic subtypes. It may sound amazing but AI systems are capable of differentiating between long-term and short-term survivors in patients who have contracted Stage-I lung cancer.
How can it do so?
It is capable of doing so by merely evaluating slides stained by eosin and hematoxylin. These days, AI is also being used in order to improve the readings of retinal images in the domain of ophthalmology. However, it would be wrong to assume that AI is only about images. The Baylor University is attempting to use AI in order to gather and analyze patient-related data from their electronic medical records.
The aim of such systems
This includes lab data. The main aim here is simple – to improve their length of stay, to identify patients who may be at risk of suffering from diseases such as sepsis, and to minimize readmissions. An AI-based organization named Virta uses data collected from patients to support dietary and nutrition management in patients who are suffering from diabetes. It is pretty clear that AI is on its way to assume a prominent place in the domain of medicine.
The next big thing
These are some of the reasons why the Council of APC (Association of Pathology Chairs) has labeled AI as the next big thing. AI is now being included in the curricula of academic departments like laboratory medicine and pathology. In fact, the clinical programs and research of these departments would be incorporating AI as well. It is expected that this would help AI shape its own future.
How can pathology use AI?
A number of editorial works in the domain of pathology have clearly encouraged pathologists to befriend this technology rather than considering it as an enemy. It is true that there can be some disruption when such a new technology is used.
However, there is nothing to deny the growing need for including such technology at the curriculum level considering the kind of opportunities that it presents. It is predicted that this particular sector would face workforce shortage in the days to come and one feels that AI can be used more than adequately to address such issues. It can also do away with monotony and thus increase the levels of professional satisfaction in this particular regard.
Would AI replace pathologists?
This seems to be one of the burning questions doing the rounds right now. However, leading experts in this domain do not really feel so. They feel that with a new tool such as AI, professionals such as pathologists, laboratorians, and cytotechnologists would be blessed with opportunities hitherto unheard of in their domains. The work would evolve in directions that are new in the truest sense of the word. In fact, experts say that in the future, pathologists would evolve to become information specialists who would no longer be merely concerned with testing slides for virus and bacteria. They would rather interpret important data, integrate information to guide clinicians, and advise on how valuable other diagnostic tests could be.
Their future roles
Apart from being important, their role as information specialists would become the very foundational basis on which the work of pathologists would be predicated in the future. This also means that in the future, they would get better at reducing the anxiety levels of the patients as well as the provider clients. They would be able to provide more timely, integrated, and accurate information. Along with the kind of human touch that they can provide as healthcare practitioners, these tools would play an important role in improving the overall system as such. This means that all the stakeholders would have a much better and fruitful experience in the end.
In the end, it can be said that research is still on regarding the ways in which AI could be applied to laboratory medicine and there are some concrete reasons as to why such technology is becoming such a prominent part of this domain.