Role of AI and Machine Learning in Healthcare Industry

Artificial Intelligence is an amalgamation of several technologies. Machine Learning is one of the common forms of AI, that works on statistical techniques for fitting models to data. Precision medicine is the most common machine learning application assisting healthcare in predicting what treatment protocols are likely to succeed for a patient.

Deep learning is another emerging technology, and a part of AI is used as speech recognition. In healthcare, Natural learning processing based application, involves the creation, understanding, and classification of clinical documentation and published research.

NLP systems can identify the unstructured clinical notes on patients, prepare reports, transcribe patient interactions, and conduct conversational AI.

Diagnosis and Treatment based Applications

Most recently, IBM’s Watson has employed a combination of machine learning and NLP capabilities for diagnosis, cancer particularly, and treatment.

Rule-based systems incorporated within the EHR system today are widely used at NHS, yet they lack the precision of algorithmic systems based on machine learning. Research labs claim that they have developed an approach to using AI or big data to diagnose and treat a disease with equal or greater accuracy than human clinicians.

Tech firms and startups are also looking forward to working on the many challenges in medical ethics and patient/clinical relationships.

Be it rule-based or algorithms, AI-based diagnosis, and treatment recommendations can be challenging to instigate in clinical workflows and EHR systems. Yet, some EHR vendors have begun to introduce AI functions into their offerings.

Patient Engagement and Adherence Applications

It has always been one of the most common problems that act as a barrier between ineffectiveness and good health. Hence, healthcare providers and hospitals usually utilize their clinical expertise in a plan to improve a patient’s health.

A survey was made of more than 300 clinical leaders and healthcare executives, and 70% plus have reported having less than 50% of highly engaged patients, and 42% of clinical experts say that less than 25% of patients showed high-level engagement.

Only a deep engagement of patients will result in better health outcomes. The growing emphasis on using AL-based capabilities can be effective in personalizing and contextualizing care.

Machine learning and business rules engines can relevantly drive subtle interventions along with continuous care.

Another focus on healthcare is an effective design of the ‘choice architecture’ to prod patient behavior in a more anticipatory way. To make treatment pathways effective, information is reached through different devices and systems. Further recommendations can be offered to providers, patients, nurses, call-center agents, and care delivery coordinators.

Implications for The Healthcare Workforce

Other than patient care, AI is taken to lead to automation of jobs and substantial displacement of the workforce. Unlike the concern of job replacement, there have been no such jobs eliminations by AI in health care.AI into clinical workflows, and EHR systems will make healthcare jobs automate that involves dealing with digital transformation, radiology, and pathology. Deep learning models in startups and labs will assist in specific image recognition tasks.

In radiology, experts can use AI can perform image-guided medical interventions such as cancer biopsies and vascular stents, define technical parameters of imaging examinations to be performed, related findings from images to other medical records and test results, & many other activities.

Clinical processes for employing AI-based image work. Imaging technology and deep learning algorithms offer the probability of a lesion, probability of cancer, nodule’s feature, or its location.

Moreover, deep learning algorithms are efficient in image recognition requiring labeled data. Millions of data through images received from diagnosis of cancer, a broken bone, or other pathology. Finally, substantial changes requisite in medical regulation and health insurance for automated image analysis.

Ethical Implications

Decisions in healthcare were made by humans, until now where AI algorithms, especially deep learning algorithms used for image analysis – which are virtually impossible to interpret. Deep learning algorithms and even physicians can diagnose cancer.

There are likely to be incidents in which patients may receive medical information from AI systems that they received from an empathetic clinician. Since predicting the greater likelihood of disease is subjective of algorithm bias, sometimes as gender or race, which should not be causal factors.

We will expect to see ethical, medical, occupational, and technological changes with AI in healthcare. Healthcare institutions as well as regulatory bodies must monitor key issues, react responsibly & establish governance mechanisms to limit negative implications. AI is proving more powerful and consequential technologies in order to influence human societies, hence it will need continuous attention and thoughtful policy.

To Conclude –

AI will continue to play a role in healthcare domains. Machine learning, which is behind the development of precision medicine, is sorely needed for advanced care. Speech and text recognition are other aspects of AI that have been employed for tasks like patient communication and capture clinical notes, increasing their usage. AI will seemingly augment the efforts of care for patients and will not replace humans as feared.