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Artificial Intelligence in Hospitals

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Picture archiving and communication system (PACS) is a medical imaging system used mainly in hospitals to store and digitally transfer images and clinically-relevant reports safely. The use of PACS reduces the need to manually file and store, retrieve and send sensitive information, films and reports. Instead, medical documentation and images can be safely stored in off-site servers and securely accessed virtually from anywhere in the world. PACS are increasingly crucial as the volume of digital medical images grows throughout the healthcare industry, and data analytics of those images becomes prevalent.

While radiologists have predominately used PACS radiology traditionally being the most prolific producer of X-ray images, PACS have joined into more departments, for example, oncology, nuclear medicine imaging, cardiology, pathology, and dermatology.

 Images are recorded and analyzed for clinical review, diagnosis and treatment as part of a patient’s treatment plan. The information gathered can be used to recognize any anatomical and physiological deformities, chart the progress of treatment and grant clinicians with a database of regular patient scans for later reference. 

Having digital access to the all-latest version of a patient’s medical images, clinical reports and history can expedite and improve care, lessening the likelihood of treatment and prescription errors and preventing redundant testing.

PACS components are hardware imaging machines; a network for the distribution and exchange of patient images; a workstation for viewing, processing and interpreting images; and electronic archives for storing and retrieving images and documentation and reports.


PACS main uses. The technology:

replaces the need for hard-copy films and management of physical archives. 

Allows for remote access, enabling clinicians in different physical locations to review the same data simultaneously.

While Offering an electronic platform for images interfacing with other medical automation systems such as a hospital information system (HIS), electronic health record (EHR), and radiology information system (RIS), it also Allows radiologists and other radiology and medical personnel to manage the workflow of patient exams.

AI is poised to change the method of medicine as we know it. However, it is a complementary technology, designed to enhance the performance of humans, including physicians, nurses and medical researchers in performing their duties.

Currently, health care is pursuing other industries in the acceptance and application of AI. However, many experts predict it will be the industry most disturbed by AI in the upcoming decade, gratitude to the broad adoption of electronic healthcare records (EHRs) and the tremendous volumes of data at our disposal.

At the new Centre for Clinical Artificial Intelligence, and other AI-centric research centers around the globe, researchers are developing system learning capabilities that, eventually, will enhance how we diagnose and treat patients. These abilities also create value and time efficiencies that improve the overall patient experience and help break down boundaries to care.

With AI as a device inside our medical bags, we will have the chance to save and improve more patient lives. Indeed, the possibilities are endless.

Currently, the team is using AI to find the hidden pearls of wisdom buried inside massive reams of data. At the same period, they are working to create a new, hybrid role what we call “physician data scientists” who understand machine learning, AI and how these technologies can utilize to medical research and clinical practice. They aim to enhance patient outcomes and drive down costs.

Results, to date, have been auspicious. Researchers are developing machine learning standards that use AI not only to predict specific patient issues but to lead straight to actions that improve patients’ health. For example, they have been able to recognize patients at high risk of death within 48 to 72 hours of hospital admittance, which allows clinicians to take proactive measures to treat them in ways that decrease other risks.

In another project, they have developed a personalised prediction model that surpassed existing prediction models for myelodysplasia syndromes (MDS). They can discover, with high degrees of accuracy, an MDS patient’s jeopardy of mortality, as well as the risk of conversion to acute myeloid leukemia (AML), a new aggressive kind of bone marrow cancer.

By recognizing the likelihood of a patient’s prognosis, they will be ready to develop a therapy plan that is more suitable for his/her condition. That means fewer cases of over-or under treatment, better counsel to patients and more personalized care.

However, AI in health care has its difficulties, too, given the level of complexity and nuance in this area. Also, given a shortage of regulatory and clinical standards in AI research until today, the range can provide inconsistent or flawed studies that could lead to the improper or negligent implementation of the conclusions.

AI is not a panacea. That is why, in their sense, people will never see “machine” physicians, because the human factors of empathy, common sense and instinct so usually present a significant role in medical decision-making. What they are doing with AI, in essence, is striving to better harness data to gain additional critical insights that could lead to improved care and outcomes.

Their work is progressing, but for them to truly move this effort forward, they must get more physicians engaged. Furthermore, they have to train them on how to understand better these algorithmic models and what the outcomes mean for research or patient care.

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