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human error in recording data and providing medications in the correct dosages.

Early enthusiasm for AI use in healthcare faded during the 1990s, be-cause cost and computer performance and networking limitations prevented early systems from delivering all of the promised benefi ts, says Ed Shortliffe, a well-known physician, biomedical informatician, and computer scientist, as well as ex-president of the AMIA (formerly called the American Medical Informatics Association).

Now though, improvements in AI technology, processing power, net-work speeds, storage, and the keeping of electronic health records (EHR) are increasing AI use in healthcare. Techniques such as natural language processing (NLP), decision support,

neural networks (NNs), and expert systems are being implemented.

The technology is frequently em-bedded into various types of medical software, tools, and equipment.

It is being employed to improve treatment workflows, enable data exchange among different EHR data-bases, and make sense of reams of information that new diagnostic techniques, as well as genomic and protein-analysis tools, collect.

Nonetheless, AI use in healthcare still faces signifi cant challenges.

Early DaysDuring the 1970s and 1980s, re-searchers began looking at utilizing AI to improve medical decision-making. Early programs used expert systems to help with clinical decision support.

For example, University of Pitts-burgh researchers developed Internist-I in 1974 to improve general diag-nostics. Stanford University scientists designed Mycin in 1976 to help doc-tors choose the best antibiotic for dif-ferent types of infections. And a Stan-ford team released Oncocin in 1979 to help diagnose whether a set of pa-tient symptoms might indicate cancer.

Shortliffe, who helped develop My-cin and Oncocin, says these early ap-plications led to considerable media interest and outside investment.

However, high costs and limited networking capabilities restricted these applications’ usefulness. A phy-sician or nurse had to take time to go from patient or treatment rooms to the centralized location of the com-puter running the AI application to get information. Moreover, users couldn’t integrate most of the early systems into their ongoing workfl ows.

Thus, interest in using AI in health-care declined until recently.

AI Healthcare ImplementationsSince then, developers have focused on making sure their AI-based med-ical decision-support systems could integrate into existing physician workfl ows, says Jack Smith, dean of

Healthcare Has Mixed Feelings about AIGeorge Lawton

For decades, AI and medical experts have discussed how AI could improve

healthcare.

They’ve said it could help doctors diagnose diseases and choose treatments,

enable better analysis of large collections of patient information, and reduce

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the University of Texas Health Sci-ence Center at Houston’s School of Biomedical Informatics.

Researchers have begun designing a new generation of systems reflect-ing the criticisms of early AI-based medical-care applications’ interfaces and about their limited networking, storage, and processing capabilities. They’ve also taken advantage of the availability of new technologies.

In some cases, AI is being incorpo-rated directly into EHR tools. In others, systems such as IBM’s Watson super-computer are providing medically related services such as speech recog-nition, decision-making, and diagnos-tics. Some applications use machine vision and NNs to analyze biopsy samples and make diagnoses.

Mayo ClinicMayo Clinic researchers are employ-ing an artificial NN to analyze large collections of patient data to diag-nose cardiac infections with fewer in-vasive exams.

The scientists trained the NN with a dataset from patients diagnosed with heart infections. A test on infor-mation from a second set of patients showed that the approach was accu-rate in 72 of 73 cases.

When used on new patients’ data, the system eliminated the need for an invasive test in about half the cases in which a doctor otherwise would have requested such a procedure.

Intelligent Predictive ToolsOther researchers are looking at AI to better analyze the enormous amount of data now stored in EHRs, and to make diagnoses and recommend treatments.

For example, the nonprofit Uhealth-Solutions is working on a suite of tools for mining such information.

The suite uses NNs to recognize pat-terns and identify patients at high risk

of potential health problems that re-quire early monitoring. It also employs genetic algorithms to look at large col-lections of disease patterns and the pro-gression of patients’ preexisting condi-tions. The algorithms test and adjust different rules that correlate the pre-existing conditions to disease outcomes.

Fuzzy logic provides a flexible way to analyze complex data. Rule-based systems recommend a progression of questions useful for identifying a dis-ease and, on the basis of the answers, suggest reference sources relevant to the suspected condition.

UhealthSolutions researchers say that using a combination of AI tech-niques improves the accuracy of disease-outcome predictions to 75 to 95 percent from the 20 percent achieved with statistical tools not based on AI.

IBM WatsonIBM has taken its Watson supercom-puter’s sophisticated NLP and query engine into the medical arena.

This application—which can sift through 200 million pages of mate-rial in three seconds—uses NLP to in-terpret spoken or textual queries and a knowledge-based system to provide decision support. It is being tested at the Columbia University Medical Center, the University of Maryland School of Medicine, and Los Angeles’ Cedars-Sinai Medical Center.

Researchers hope the system can automatically analyze patient re-cords, medical journals, and clinical- trial data to identify patterns and suggest treatments.

C-PathResearchers are turning to advanced machine-vision and machine-learning techniques to improve the visual analy-sis of microscopic images.

For example, Stanford University School of Engineering and Stanford School of Medicine scientists have

developed the Computational Patholo-gist (C-Path) to better analyze images of breast cancer cells (see Figure 1).

Traditionally, humans have been able to analyze only three potentially problematic features in these cases: the percentage of the tumor com-prised of tubelike cells, the diversity of the nuclei in the outermost cells, and the frequency of cell division.

In contrast, C-Path can analyze 6,642 factors. Using more factors af-fords greater precision in distinguish-ing between many closely related pat-terns of cancer, which yields better treatment strategies.

The researchers trained the system with data from patients whose prog-nosis was already known.

NCCDThe National Center for Cognitive Informatics & Decision Making at the University of Texas at Houston is conducting research to improve the usability of intelligent systems within healthcare workflows, as well as develop improved EHR standards, healthcare-related IT systems, and medical-device communications.

The US Office of the National Co-ordinator for Health Information Technology recently provided US$15 million to help fund the project.

To Use AI or Not to Use AIWith all of its benefits, AI use in healthcare still faces several notewor-thy challenges.

A big hurdle is the difference in the semantics that various medical- related applications use, says the Uni-versity of Texas’s Smith. Hospitals, medical personnel, and even billing systems also describe similar condi-tions in different ways.

As AI tools try to work with data from these sources, translation mistakes could lead to errors with potentially serious consequences.

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Researchers Use AI to Build GamesGeorge Lawton

A UK doctoral student has developed an evolutionary-computing ap-

proach to designing video games from the ground up.

Imperial College London PhD candidate Michael Cook developed the

Angelina (a recursive acronym for “a novel game-evolving labrat I’ve named

A ngel ina”) game -development application.

Previous research has looked at evolving only certain elements of ex-isting games. Cook says his work is the first to look at evolving multiple elements simultaneously to create new games.

Angelina uses a variant of com-putational evolution called coopera-tive coevolution, which divides a big

Better semantic-processing tools are needed to cope with these issues.

Another challenge lies in integrating AI technologies, additional types of software, interfaces, and other compo-nents into a single application that pro-vides both a smooth user experience and helpful knowledge, says Smith.

These capabilities must be deeply embedded in healthcare-related in-formation systems and workflows for them to be used successfully, says Mi-crosoft Research distinguished scien-tist Eric Horvitz.

Another concern is that the sophis-ticated new EHR systems with AI-based tools could be hard to use and could thus slow down the patient-treatment workflow. Smith says the

time doctors must spend entering pa-tient information in an EHR system could reduce the number of patients a physician sees by up to 50 percent while they are learning to use it and by 10 to 25 percent later.

And experts will have to monitor even sophisticated AI applications to make sure their diagnoses are appro-priate for a given patient’s context.

Toward an Intelligent Future?Smith predicts healthcare profession-als will increasingly employ NLP to extract useful information from text.

He also says AI will be embedded into systems and products in ways that will be easy to utilize, thereby

encouraging their use. This type of integration could eventually lead to devices that even consumers could use for health evaluation, he notes.

In the long run, Smith says, AI techniques will be necessary to make sense of the mountains of data now being collected that are too much for individuals to analyze.

Andrew Beck—a doctor who is currently a Stanford PhD student in biomedical informatics and who also worked on C-Path—says new machine-vision techniques will lead to a wealth of new information and massive databases. However, he notes, making sense of all this information will require more powerful comput-ers and AI-based tools.

Figure 1. Stanford University scientists have developed the Computational Pathologist application to study images of breast cancer cells. Traditionally, humans can analyze only three potentially problematic features. The Stanford application, on the other hand, analyzes 6,642 factors, which lets it more precisely distinguish between different cancer patterns.

Relationships between epithelial nuclearneighbors

Relationships between epithelial andstromal objects

Relationships between epithelial nucleiand cytoplasm

Characteristics ofepithelial nuclei andepithelial cytoplasmCharacteristics of

stromal nuclei andstromal matrix

Relationships between morphologicallyregular and irregular nuclei

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problem into smaller parts and solves the subparts independently in order to resolve the larger question.

The purpose is to optimize games for fun, which the system calculates on the basis of degree of difficulty and the length of time necessary to complete levels.

Angelina can generate and test up to 50,000 variations in a full game-creation cycle.

Working with AngelinaAngelina begins creating a game with all of the graphics—including enemy animations and game layout elements such as walls and trees—supplied by a human developer.

The application arranges the graph-ical elements in multiple ways and generates rules for different versions of a game.

Angelina considers matters such as the layout of game elements for each difficulty level and “powerups,” which are items that give players spe-cial abilities such as increased speed or jumping ability.

“The system is built up of multiple evolutionary processes,” Cook ex-plains, “but each process is only re-sponsible for part of the solution.”

During each step, if the difficulty-level design process develops certain characteristics, such as a particular pattern of barrier walls, other elements will evolve accordingly. For example, if the system makes walls higher, it might also give players more jumping ability.

Angelina also looks for problems with the specific game designs it creates.

During each evolutionary cycle, the system evaluates its population of games, selects the best designs, and recombines them to create the next set of candidates to test.

Cook says Angelina is effective because its processes mimic effective

game design, a cooperative task in-volving artists, designers, program-mers, and producers working inde-pendently and then coordinating ideas and influencing one another.

Angelina isn’t totally autonomous, as a human must still create the graphics, sound effects, and music that go with a game.

ChallengesAccording to Cook, a big challenge is defining a meaningful way to calcu-late how much fun a game is to play.

“At the moment,” he explains, “all I can use is basic heuristics and ideas I’ve drawn from talking to game de-signers.” He says he’s still looking for ways to improve this process, which is an important part of Angelina’s effectiveness.

Another key issue is the time and computational resources required to simulate game play and calculate fun.

Angelina has few problems with this process when working on the rel-atively simple 2D maze games with enemies and powerups for which it has been initially designed. However, complications could arise for work on graphics-intensive console or PC games.

Looking ahead with AngelinaCook says his techniques could be useful in completing and improving partial game designs that other peo-ple start.

He says techniques similar to the ones Angelina uses could be applied to games such as 3D shooters or those involving real time strategy.

Imperial College London research fellow Cameron Browne says he has been using a standard evolutionary approach, one without cooperative evolution, to evolve various board games. However, he explains, the standard techniques are inherently

random and thus don’t necessarily explore all options that could yield highly effective games.

Cook says none of the games that Angelina has developed—which are on his Games by Angelina website (www.gamesbyangelina.org)—are the equal of today’s advanced com-mercial games.

However, he notes, some are clever, and many are like early arcade games or those currently found on smartphones.

To make the games more compli-cated and fun, Cook says, he is ex-ploring ways to include human feed-back toward the end of Angelina’s game-design process, perhaps via sim-ple yes/no surveys whose responses could be fed into the application.

The next big step, he explains, will probably incorporate multiple coop-erative coevolutionary processes for each of the various design stages. An-gelina would thus start by prototyp-ing high-level game designs and then work on increasingly more specific development stages.

He says, “I hope my research will help computational-creativity research-ers build better, more complex de-sign systems. Angelina will also, over time, incorporate many other AI tech-niques and investigate their useful-ness in hard creative problems.”

Cook plans to release Angelina as open source software so that other game developers can adopt and adapt it.

“One thing the games industry could do,” he explains, “is open up its data, engines, and games to aca-demics for us to work and experi-ment with.”

“Obviously,” he adds, “I’m not ex-pecting every publisher and developer to hand everything over for free. But even a great API … could allow re-searchers to get their hooks into a real, live game and start building sys-tems to generate content for it.”

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cooperating robots capable of co-ordinating their movements, which has numerous potential military and other uses.

A lex Kushleyev and Daniel Mellinger—doctoral students in the University of Pennsylvania’s General Robotics, Automation, Sensing, and Perception (Grasp) Lab—designed the “quadrocopters.”

Kushleyev’s and Mellinger’s advi-sor, professor Vijay Kumar, presented the robots’ first musical performance, of the James Bond theme song, at a recent TED conference (see Figure 2).

The devices coordinated their ac-tions well enough to play drums, a keyboard, maracas, a cymbal, and a guitar-like instrument in harmony.

System AnatomyThe researchers built the flying ro-bots with accelerometers to sense movement, gyroscopes and 3D mag-netometers to measure rotation, and barometers to detect height, accord-ing to Mellinger.

The small, agile aircraft used in the musical performance run for 11 min-utes on a charge, while larger drones

Robots Coordinate Flight Paths to Play MusicGeorge Lawton

Two US graduate students have developed a team of small flying robots

that can coordinate their movements on their own well enough to play

musical instruments.

The research addressed the challenges of precisely controlling swarms of

used in other experiments run for up to 30 minutes, he notes.

Each quadrocopter runs the open source Robot Operating System on an ARM7-based low-power proces-sor. The researchers used these rela-tively slow processors to keep costs down.

A motion-tracking product from Vicon Motion Systems uses machine vision to estimate the location of each drone on the basis of video from mul-tiple ceiling-mounted cameras in the room where the robots are flying.

The system feeds this positional in-formation to the base-station com-puter, a standard PC that acts as a centralized control server. It issues commands to each drone using the ZigBee low-power, low-speed wire-less communications technology.

The control server calculates a function representing the intended collective movement of the group, based on instructions from the re-searchers. It then feeds the func-tion to each drone’s less powerful,

Figure 2. Two University of Pennsylvania doctoral candidates have designed flying robots that coordinate their motions on their own sufficiently well to play drums, a keyboard, maracas, a cymbal, and a guitar-like instrument in harmony.

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processor-based onboard controller. This controller directs the robot’s path so that it avoids obstacles and is in the right place at the right time for the task it’s performing.

The control server continues to re-ceive positional information from the motion-tracking system and, if the drones stray off course, sends them new commands.

A program running on the drone interprets high-level commands from the control server and translates them into changes to each of the drone’s four rotors—which can turn at dif-ferent speeds from one another—at up to 600 times a second. This en-ables precise aerobatic movements.

The system’s architecture distrib-utes the various tasks to the most appropriate elements and thereby

reduces latency and works better than using a single controller for all functions.

RamificationsThe swarm-control project builds on previous Grasp Lab work on develop-ing precise controllers that enable in-dividual robots to perform acrobatic maneuvers.

As part of the project, the Grasp team has developed algorithms for enabling drones to hold objects on a flying platform, which could tilt to the side if the aircraft doesn’t fly evenly. In the quadrocopters’ musical performance, this capability let some robots work together to carry sticks and use them to hit the cymbals.

A big challenge lies in developing a system that works outside a closed,

laboratory-based motion-capture en-vironment, according to Mellinger. This would require a different motion-capture system, he says.

In the long run, he noted, this tech-nology could have many useful appli-cations in areas such as agriculture and infrastructure inspection.

Of the small robots, Kumar adds, “They can operate indoors, in tightly constrained environments. We are in-terested in using them for search and rescue, disaster recovery, and other missions in which it is too dangerous for human workers to operate.”

The smaller robots are not yet available for purchase, but the Grasp team’s larger, 3-foot-long drones are being sold by the group’s spinoff company, Kmel Robotics, Kumar says.

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