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  • 15 Aug, 2022

AI impact on human health

The most significant challenge to AI in healthcare is not about technologies being useful enough but assuring their adoption in day-to-day clinical practice.

Many research studies point out that AI can function as well as or even better than humans at key healthcare assignments, such as diagnosing diseases. Nowadays, algorithms are already outperforming radiologists at discovering malignant tumors and guiding researchers in how to make cohorts for costly clinical trials. Regardless, for many reasons, we think there will be substantial years before AI replaces humans in a general medical approach, but AI's impact and benefits in healthcare's future are inevitable.

What are the domains that it can cover?

Artificial intelligence is not a single technology but rather a collection of them. Most of these technologies have immediate applicability to healthcare fields, but the detailed methodologies and tasks they support may vary widely.

We believe in AI's essential role in the healthcare offerings of the future. Machine learning is the primary capacity behind the evolution of precision medicine, widely consented to be a sorely required advance in care. Early efforts at diagnosing and treatment guidance confirmed to be challenging, but we anticipate that AI will eventually master that territory as well. Given the fast progress in AI for imaging analysis, it is likely that most radiology and pathology images will be, in the future, examined by a machine. Speech and text recognition, for example, are used already for patient communication and clinical notes.

Prevention

Yearly, around 400,000 hospitalized patients suffer from preventable diseases, with 100,000 deaths. Therefore, the promise of improving the diagnostic approach is one of AI’s most compelling healthcare applications.

Incomplete medical records or large caseloads can sometimes lead to harmful human errors. Untouched by those variables, AI can anticipate and diagnose the condition at a rate faster than most medical professionals.

Drug discovery

The pharmaceutical development industry is getting blocked by rocketing development expenses and research that takes thousands of hours. Placing each drug under clinical trials costs an estimated average of $1.3 billion, and just 10% of those drugs successfully make it to the market.

Due to technology breakthroughs, biopharmaceutical companies are noticing fast the efficiency, precision, and understanding AI can provide.

Diagnosis and treatment

Diagnosis and disease treatment has been AI's priority since the 70s, with the MYCIN developed at Stanford for diagnosing blood-borne bacterial infections. That was promising for accurately diagnosing and treating disease but was not embraced for clinical routine. The first results were not substantially better than human diagnosticians. Also, inadequate integration with clinician workflows and medical history systems was at play.

In the modern economic climate, tech companies and startups are performing better and better on the same issues. Google, for instance, is cooperating with health delivery networks to create prediction models from big data to alert clinicians of high-risk disorders, like sepsis or heart failure. Google, as well as Enlitic, are also developing AI-derived image analysis algorithms. Jvion delivers a ‘clinical success machine’ that determines which patients are most at risk. Each of these firms could supply decision aid to clinicians pursuing the best diagnosis and treatment for patients.

Is AI replacing human clinicians?

On a large scale, we think AI technologies will not replace human clinicians but rather extend their efforts to care for patients. In time, clinicians may shift toward tasks and job designs that pull on uniquely human aptitudes like empathy, persuasion, and big-picture integration. Possibly the only healthcare providers who will find it hard to keep their jobs over time may be those who decline to work alongside AI.

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