Healthcare is one industry segment where artificial intelligence and machine learning are proving to be transformational. Now is as good a time as any for healthcare to invest more in AI/ML and experience all round gains.
Healthcare an Ideal Candidate for AI/ML Development
AI and ML, in the hands of the right development, lead to:
-
Accurate predictions, trend predictions and analysis
-
Image recognition
-
Minimum manual intervention and fine-tuned precise diagnosis
Healthcare is one area that has data in voluminous amounts. This data is different compared with data in business environments. ML, with its supervised or unsupervised learning models and a set of algorithms to rely on, can tackle all these with far more precision and speed:
-
Data flows from sensors in various equipment; data is contained in MRIs, sonograms and ECG/EEGs.
-
There are plenty of variables internal to such data and external to it such as genetics, demographics, age and personal lifestyle.
-
The purpose of data could be the diagnosis for therapy, diagnosis for prevention, for research, for drug efficacy and clinical trials and so on.
-
It is accepted that there is more contained in data from sensors such as radiograms and MRI that human beings would find difficult to analyze and assess in a short time. ML can do it fast and even detect anomalies or map covariates that a human analyst would not see or discover or do it fast enough.
In order to know just how immense the possibilities of ML are, one can take a brief look at all the work done so far in healthcare using machine learning and artificial intelligence. Researches and papers are a pointer of things to come and of the capabilities of ML for healthcare.
ML for Non-Linear ECG Signal Analysis
The ECG machine puts out a non-linear graph that is difficult to quantity on time and frequency domains. It would require a skilled cardiologist to visually interpret the ECG trace. Researchers have developed a machine learning wavelet transform decomposition method to carry out analysis and statistical validation. There is quite a bit of sophisticated technology and processes in use but the end result is that the system can be used for web-based telemedicine to remotely monitor patients. Work is underway in ML for use of the genetic algorithm for unsupervised classification of ECG charts.
Computerized Diagnosis of Colon and Lung Cancer Using Pixels
ML is ideal for pixel-based computerized diagnosis of lung and colon cancers. Any such application depends on the availability of a large amount of data that shows the normal condition, lesions, abnormal lesions, malign and benign lesions to learn from. ML technologies that study the lesions at pixel levels lead to reduced errors that arise from imprecise feature calculation. Machine learning development services work on images derived from CT scans and speed up automatic diagnosis with minimum human intervention.
Down to Earth Rehabilitation Plans for Home Care Patients
The above two examples show just how ML can transform diagnosis. The speed and accuracy could lead to cost savings for hospitals and it can prove to be a lifesaver too. However, at a more down to earth level, ML can help deliver personalized rehabilitation plans, especially the elderly with a variety of medical conditions. Doctors may gloss over such facts and make partly accurate or suitable plans but with ML there is a certainty that the recommendation will suit each individual. Further, the rehab solution could also gather data and result in all-round improvements in the delivery of healthcare in this segment.
Fraud Detection Becomes Accurate
ML can be implemented in various ways and one of them is to detect fraud. Fraud occurs at several levels in health care such as non-availability of services when required leading to grave consequences for the patient. Doctors may collude with patients to defraud insurance companies in ingenious ways. Insurance companies providing health insurance cover could save millions of dollars simply by investing in supervised fraud detection systems rooted in machine learning.
Carotid Artery Image Segmentation
Sonography of the carotid artery gives information about the thickness of the wall at various points. The trick is to know at which point the thickness should be measured. ML implementation in sonography brings about dramatic precision of results by analyzing image at the pixel level. Such machine learning incorporation reduces the complexity of the system and delivers more accurate results in less time with minimal human intervention. ML could be interpreted at the machine level itself by manufacturers of the machine. In that case, manufacturers would have the opportunity to provide a similar analysis of organs and tissues—a stupendous challenge and an expensive one too. Hospitals, however, can choose to add such solutions to their diagnostic chain with the help of Machine Learning development services.
Not All ML Developers Are the Same
Healthcare presents unique sets of issues for ML solution development.
-
One prime requisite is to have access to huge sets of patient data from which the ML system can learn and this brings in issues of confidentiality and security as well as integrity on the part of the ML services provider.
-
ML itself makes use of a variety of algorithms and how models are applied to test data to derive accurate results.
-
ML systems are prone to delivering false truths.
-
One of the biggest challenges is that ML experts are no doubt extremely good at what they do but they severely lack knowledge on the medicine side and this could be an impediment to ML solution development. It would take an ML team some time to become familiar with medical terminology and processes.
Still, from what has been happening so far, ML promises great transformations in healthcare. Research papers hint at what ML is capable of and some health care services have already implemented ML in various areas. However, there is a feeling that insurance companies could do more to propagate ML developments in healthcare. After all, they stand to gain by way of reduced claims when healthcare transforms due to progressively higher ML implementations.
healthcare workers talking -DepositPhotos
