Artificial Intelligence (AI) and The Future of Medical Care
Does the future of medical care look doctorless?
In reality, a totally doctorless society is not forthcoming. While the practice of medicine is being transformed with the introduction and constant development of artificial intelligence, technological advancements in medical analytics, robotics, remote health services are essentially helping the fraternity address key challenges to reshape the economics and accessibility of healthcare.
Let’s dive in:
Artificial Intelligence has permeated the medical industry in various ways. AI driven medical billing solutions now influence payment processes while a range of AI-infused solutions are commonly used to enable clinicians to improve detection of illnesses and different types of cancers and this is representative of only a few positive outcomes of AI in medical care.
Recently, HOPPR secured $31.5M Series A to scale AI Infrastructure for medical imaging while the UiPath Medical Record Summarization AI agent, launched first in private preview for customers, was introduced to empower both payer and provider organizations to take total advantage of the combined power of gen AI and agentic automation in this field.
Practitioners across most areas of medicine are now slowly starting to use AI to help them recognize symptoms and make better (albeit quicker) decisions that will improve patient care and enhance treatment plans. These technologies are being trained to collect and analyze everything, from medical images to patient histories, to decode lab findings and genomic data faster, with the aim of offering a more holistic view of what’s wrong with a patient.
Understanding The Core Needs and Benefit of AI in Medical Care
In reality, Artificial intelligence in various forms has been used in medicine over the years in different ways and experts now predict that the adoption of large language models will steadily reshape the scope of medicine and healthcare down the line.
Most of these AI effects are expected to bring positive changes, and increased efficiencies. What doctors and AI innovators are most excited about is how AI can be trained to help reduce both costly and common mistakes in medical care, and how it can ease the ongoing crunch in medical care.
AI tools today are being used to help ease hospital administrative burdens too, creating better spaces for doctor to patient interactions to be the main focus while the administrative tasks largely get handled by the technology.
Globally, the medical system has been said to be broken in many ways, a key aspect of concern being the constant problem of fewer doctor-to-patient ratios. This puts pressure on every patient-doctor relationship either affecting how freely a patient can converse with their doctor or how much time and attention a doctor can devote each patient.
In this regard, the overall medical system would greatly benefit from a good balance of AI working in tandem with its human workforce to ensure better medical processes are put in place to ease workflows and meet patient needs, demand and expectations on time.
AI, Modern Medicine And The Near-Future
Given current AI abilities, one way doctors can benefit from AI models is by using it to second-guess themselves in times of a tricky case. Call it a sound boarding model if you will. Everyone needs that at some point.
Another growing opportunity stems from AI’s ability in notetaking and clinical or consultative summarization, facilitating better in house processes for doctors on call or those who work as general physicians especially since they meet several patients in a typical work day.
Improved automation of notes and medical summaries would benefit global healthcare workers in many ways, it can ease the paperwork load and help reset the doctor-patient relationship too. Freed from the note-taking process, doctors could sit face-to-face with patients and focus on the actual conversation, opening a path to stronger, more meaningful connections, something that many patients may crave especially when dealing with critical health issues.
New age AI Systems for ambient documentation will also soon be able to listen during patient visits, to record what was said and done, then generate an organized clinical note summary in real-time. If symptoms are discussed in the conversation, the AI would be able to suggest diagnoses and possible courses of treatment too.
Once ready, this can be reviewed by the attending physician before any actual medical action is taken because all said and done, the need of the hour lies in learning how to optimize use of artificial intelligence in the medical ecosystem without it necessarily replacing human doctors and medical experts.
In certain areas, AI is now being developed to support data analysis for better diagnosis of medical scans and images. As Doctor Amit Parasnis, Head of Oncology at Manipal Hospitals, Baner (India) puts it, AI can’t give a complete diagnosis yet but maybe in future it will, based on a patient’s preexisting symptoms, the process is on the way. AI is fed patient data, like age, scan and biopsy data and based on that learning an AI would potentially be able to share a full diagnosis in future.
Doctor Amit Parasnis adds, “AI or machines are useful in a few more areas, maybe to an extent in biochemistry. Today, you put a blood sample in a machine and it gives you a full analysis. But for a tissue sample (biopsy) to be done, a pathologist manually assesses the tissue to test it. But for AI to do that, it’s not happened yet but the process is on the way.”
The Growing Impact of End-to-End AI Systems In Medical Care: China’s Self-serve Medical Kiosks
Recently, China presented the world with a range of (pretty neat!) AI-powered, doctorless kiosks , machines that scan, diagnose, and even dispense medicine in minutes. There is no real need for a human doctor, these systems can do what a human doctor would have.
These machines could steadily transform access to medical care, making it easier and quicker for patients to get timely treatments, something that could be truly beneficial in rural or hard to reach areas to begin with.
But the question remains, can or rather, should technology honestly replace the human touch in end to end medical care? Would patients prefer a humanoid bot to an actual human doctor as the years go by? Say 10 years from now, a patient would still want to be treated by a human doctor but not necessarily a humanoid, we are not there yet, says Doctor Amit Parasnis. If you need a wound that needs suturing, doctors don’t see AI doing it anytime soon, he adds. Humans will still prefer a human doctor in the coming years. But, for certain biopsies that need to be done from inside the body from afar, medical experts have been using remote controlled soft tissue bots for these procedures and to provide medical aid from miles away already.
What Should Be Kept Foremost In Mind As The Development and Use Of AI Grows In Medical Care?
Data sets today can be largely biased, reflecting societal imbalances in terms of culture, race and more. Without correction here at the fundamental level, the biases will be cemented in the foundation of how AI models are built to drive healthcare prospects.
There’s also the problem of AI hallunications, AI can often make up ‘’facts’’ when fed large quantities of data and then present them as if they were real. In a sensitive area like medical diagnosis or care, this would hurt future development of AI powered systems if not kept in check at the right time, which is now.
But that’s not to say that the technology isn’t powerful. It boils down to how the medical fraternity will build with it without hurting the basic core and fundamentals of healthcare and patient needs.
Other top of mind concerns surrounding AI and medical care involve:
The Need for the Human Element: AI-human Balance
It is critical for AI to complement, and not replace, the actual human expertise in critical industries like healthcare. Proper human oversight can then ensure the responsible and ethical use of AI in sensitive or high-stakes situations too.
Preventing Data Biases, Focusing on Accuracy
AI models are as good as the data they are fed and trained on. Focusing on the quality of data is important to avoid bias and ensure future AI models are built with the aim of providing realistic output.
AI and Patient Ethics
Mechanisms need to be put in place to appropriately address any AI related misconduct or data falsification given how AI hallucinations is already a real challenge today. Additionally, as Doctor Amit Parasnis puts it, “AI won’t necessarily affect ethics in healthcare unless it results in breach of data because decision making and interpretation of results still has to be done by a clinician as of today. The data can be leaked and a breach of privacy is not something any patient would like,” he adds.
Bridging The Skills Gap
As AI advances, the need to rethink how medical training across fields within this ecosystem are undertaken will grow. It will become crucial to reconfigure plans and restructure training norms to have the current student population learn how to effectively use these tools and to use prompt engineering as part of their daily task.
In India for instance, medical students are trained on simulators that are sometimes powered by AI to enable reasoning and training for future situations. But this is still at a nascent stage.
Medical Validation With Expert Input
It is critical now more than even to have seasoned doctors thoroughly test and validate AI tools in the making or the information coming from it to ensure accuracy and reliability before any of them, the tools or the AI powered results are implemented.
End Word
The fact remains; AI in medical care and the increased use of machine learning and natural language in processing and analyzing complex medical data, will lead to faster, more accurate diagnoses, it will help personalize treatment plans, and also improve drug discovery. Today’s AI is being regularly reframed and built to help analyze medical images, predict disease risks, assist in robotic surgeries, automate administrative tasks, and much more. While the future of AI and its use in healthcare will offer significant benefits, responsible AI implementation will require constant attention to challenges like data privacy, bias, and the need for actual human and doctor intervention.
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